Elucidating the biological characteristics of overweight populations based on urine Raman spectroscopy and bioinformatics analysis.

Bulge DNA-driven CRISPR/Cas12a dynamic activation circuit enables highly sensitive and versatile biosensing.

The histone-modified landscape: Core mechanisms of osimertinib resistance in EGFR-mutant lung cancer.

Effect on disease activity of ofatumumab in the treatment of glial fibrillary acidic protein astrocytopathy and 18F-DPA714 PET/MRI imaging assessment.

Therapeutics and vaccines involving mRNA have shown significant progress over the last five years. These lipid nanoparticle-based formulations introduce substantial analytical complexity, as multiple Quality Attributes (QAs) must be monitored to ensure product efficacy, stability, and safety. Encapsulation efficiency (EE) and mRNA integrity are particularly critical, yet current analytical approaches often require separate assays, challenging sample handling, and limited selectivity toward free versus encapsulated species. Therefore, new chromatographic methods capable of resolving these species and supporting multi-payload formulations remain a major unmet need. To rapidly obtain multiple QAs for mRNA-LNP formulations, we present an optimized two-dimensional liquid chromatography (2D-LC) workflow combining anion-exchange chromatography (AEX) in the first dimension (1D) and ion-pair reversed-phase liquid chromatography (IP-RPLC) in the second dimension (2D). The 1D-AEX evaluated encapsulation efficiency of mRNA within lipid nanoparticles based on the mRNA ratio of intact and disrupted drug products, while 2D-IP-RPLC assessed integrity profiles and mRNA-lipid adducts. Attention was paid to the 2D optimization to eliminate solvent incompatibilities and ensure efficient transfer between dimensions. This approach enables simultaneous determination of EE, mRNA integrity, mRNA-lipid adducts, and transcript ratios in multi-payload mRNA-LNP formulations. It also provides chromatographic assessment of free mRNA integrity within formulations. This method furthermore enables the characterization of additional species and confirms the presence of surface-associated mRNA. The workflow demonstrates good selectivity and applicability to both mono- and multi-payload mRNA-LNP products. Overall, the developed 2D-LC platform is a powerful analytical tool for comprehensive mRNA-LNP characterization. By enabling simultaneous assessment of several critical QAs, including EE, integrity, transcript ratios, and mRNA-lipid adducts, it streamlines analytical workflows and reduces reliance on multiple independent assays. This approach provides mechanistic insight into LNP structure, including detection of surface-associated RNA species, and establishes an innovative tool to support formulation development, process optimization, drug products release, and stability studies for next-generation mRNA vaccines and therapeutics.

Machining performance investigation on 17-4PH steel material with innovative textured tools.

BAHD acyltransferases constitute one of the most versatile enzyme superfamilies in plants, catalysing the acylation of alcohols, amines, polyamines, and phenolic compounds to generate an extraordinary diversity of specialised metabolites. Initially identified through a limited number of anthocyanin- and alkaloid-modifying enzymes, BAHDs are now recognised as key regulators of phenylpropanoid flux, cutin and suberin polymerisation, volatile ester biosynthesis, and the stabilisation of acylated flavonoids. Comparative genomic analyses classify BAHD proteins into eight clades that share conserved catalytic motifs yet display pronounced functional divergence, reflecting a balance between deep evolutionary conservation and lineage-specific innovation. Recent structural and biochemical studies demonstrate how subtle active-site modifications govern substrate promiscuity and specialisation, enabling rapid metabolic reprogramming during environmental stress. Omics-based investigations further reveal widespread induction of BAHD genes under drought, salinity, heat stress, pathogen attack, and herbivory, linking BAHD activity to cell wall reinforcement, phenolamide biosynthesis, anthocyanin acylation, and ecological signalling. Beyond their physiological roles, BAHD acyltransferases have emerged as attractive targets for metabolic engineering, synthetic biology, and crop improvement, where manipulation of specific family members enhances stress tolerance, biomass quality, and nutritional or industrial value. Here, we integrate evolutionary, structural, and regulatory insights into BAHD function, highlight emerging translational opportunities, and discuss challenges associated with functional redundancy, substrate promiscuity, and biosafety considerations. Collectively, this synthesis positions BAHD acyltransferases as central mediators of plant adaptation and as promising tools for sustainable agriculture and biotechnological innovation.

Civic technologies are increasingly promoted as tools to enhance democratic participation, decision-making, collective action, and digitally enabled governance. This study examines the emergence of AI-enabled multilingual civic platforms and their potential to support inclusive participation and social innovation in linguistically diverse democracies. The study adopts a conceptual and exploratory research design, supported by qualitative analysis, to enhance the theoretical clarity of contested concepts such as digital empowerment and social innovation. A systematic review of 158 civic technology platforms was also conducted to examine the role and limitations of major public resource repositories in promoting multilingual democratic processes through three dimensions: social innovation, digital democracy, and civic technology. The findings show that instrumentalist approaches to social innovation – prioritising efficiency, performance, and scalability of civic tools implementation – predominate over approaches that emphasising empowerment, deliberation, and changes in social and governance relations. Most platforms are technology-driven rather than human-centered, with limited attention to inclusion, linguistic diversity, and meaningful participation. Consequently, their capacity to enable transformative forms of social innovation remains constrained. The study concludes that while AI-enabled multilingual platforms may reduce language barriers and facilitate the participation of minority and vulnerable groups, their democratic and socially innovative potential depends on the alignment between technological and user design, governance arrangements, and the extent to which they enable inclusive participation and knowledge co-creation.

The article discusses the features of using digital educational games as an innovative tool for assessing students' knowledge in mathematics lessons. It analyzes the advantages of interactive gaming technologies in motivating students to learn, developing logical thinking, and improving the effectiveness of knowledge assessment. Criteria for selecting digital games for knowledge assessment are identified, and methodological recommendations for their integration into the educational process are outlined.The purpose of the article is to determine the pedagogical and methodological features of using digital educational games in the process of assessing students' knowledge in mathematics lessons, as well as to justify their effectiveness in the context of the modern educational environment.The article discusses the features of using digital educational games as a means of assessing students' knowledge in mathematics lessons in general secondary education institutions. The relevance of introducing gaming technologies into the educational process is substantiated. A comparative description of digital educational games that are best suited for studying mathematics in elementary school is presented. Prospects for further research related to the development ofmethodological models for integrating digital games into the educational process are outlined.It is concluded that currently in Ukraine, the introduction of digital games into the process of teaching mathematics raises many controversial issues related to various aspects, in particular: defining the goals, place, and limits of using digital didactic games in the educational process; the readiness of mathematics teachers for the methodically balanced use of digital games in the educational process; the conscious choice of software products.t found

Arsenic (As), particularly in its bioavailable species of inorganic As(Ⅲ) known for its significant toxicity and mobility, is a carcinogenic risk to humans. Serving as an innovative analytical tool, the diffusive gradients in thin films (DGT) technique facilitates on-site detection of bioavailable metals. However, the options of DGT binding gels tailored for As(Ⅲ) detection remain elusive. This study undertook the modification of tetraethyl orthosilicate with a 3-mercaptopropyl-trimethoxysilane to synthesize cost-effective nanomaterials endowed with the capacity for selective adsorption of As(Ⅲ) through copolymerization and atmospheric pressure drying. The synthesized materials were systematically characterized using X-ray diffraction, scanning electron microscopy, and C/H/N/S elemental analysis, revealing a specific surface area of 472.39m2/g, a particle size of 12.7nm, and a loading capacity of 3.115mmol/g. The DGT combined with gel prepared from this material has a specific affinity for As(III) without adsorbing As(V).Its adsorption efficiency for As(III) may reach 84.5% within the first hour. This DGT technique has a linear detection range of 0.5-15mg/L (R2 = 0.99995), an elution recovery of 85.1%-102.7% (RSD < 10%, n = 3), and a maximum adsorption capacity of 301.7μg/cm2, meeting requirements for long-term environmental monitoring. The experiment studied the effects of interfering factors, including pH values ranging from 3-9, ionic strength of 10-500mmol/L NaNO₃, Fe2⁺ 0-5.0mg/L, Mn2⁺ 0-1.0mg/L, As(V) 0-5.0mg/L, dissolved organic carbon 0-20.0mg/L, SO₄2⁻ 0.55-1.10g/L, and PO₄3⁻ 5.0-10.0mg/L. The device maintained a stable treatment performance under all these conditions. When the DGT device was deployed in spiked river water, estuarine water, and seawater, a comparison of its adsorption performance with existing methods showed that the device had a comparable adsorption performance and demonstrated excellent long-term stability in practical water environment applications.

The rapid development of digital tools for medical education and training has expanded the possibilities for flexible and personalized learning, but it has also created challenges in choosing the best tools to meet specific learning needs. This article presents a functional framework that assists educators and learners in selecting tools based on their needs and the learning context. By categorizing tools into synchronous and asynchronous types, we aim to simplify the decision-making process and optimize educational experiences. Synchronous learning tools like YouTube Live, PathCast, Zoom and Microsoft Teams support real-time participation and promote communication, collaboration and responsiveness in the learning environment. These tools are especially useful for live streaming, Q&amp;A, and discussions where instant feedback can support learning outcomes. Asynchronous learning tools such as YouTube, Canvas, Elicit and Quizlet, give learners the flexibility to learn material at their own pace, encouraging independent learning and memorization. Asynchronous platforms allow for pausing, reviewing and repeating material as needed, making them ideal for complex courses that require effective repetition. Hybrid models, especially the flipped classroom approach, blend these modes effectively. Tools like Edpuzzle and Nearpod make it possible to create interactive lessons and exercises in the classroom. The integration of innovative technology in medical education is not solely about adopting new tools but also about promoting adaptability and a culture of continuous learning. Blending synchronous and asynchronous tools with a human-centered approach, can build flexible, responsive learning environments that empower educators and learners to make informed choices, creating adaptive and effective learning environments that meet diverse needs.

Stem cell-based therapies are contemporary approaches that particularly involve the use of mesenchymal stem cells. Mesenchymal stem cells are multipotent precursors capable of differentiating from various cell sources, including adipocytes, myocytes, osteoblasts, and chondrocytes. They have self-renewal capabilities and possess properties that support hemostasis, reduce inflammation, modulate the immune response, and promote tissue regeneration. The paracrine activity of mesenchymal stem cells serves as a significant mechanism for their therapeutic efficacy. Recently, there has been a growing interest in the secretomes of mesenchymal stem cells which consist of growth factors, cytokines, chemokines, and extracellular vesicles. Extracellular vesicles are classified into two subtypes: ectosomes and exosomes. These secretions hold potential as innovative tools in regenerative medicine and dentistry for diagnosing, managing, and monitoring various diseases. This review focused on mesenchymal stem cells-derived exosomes and their potential as an alternative strategy for the therapeutic management of temporomandibular disorders. It also summarized the benefits and drawbacks associated with this treatment

Low numeracy literacy skills in early childhood, such as difficulty recognizing number symbols and quantity concepts, pose a challenge in preparing for primary education. This study aims to improve the numeracy literacy skills of children aged 5–6 years through the use of Pop-Up Book media. The research method employed was Classroom Action Research (CAR) using the Kemmis and McTaggart model, conducted in two cycles. The subjects were 19 children at KB Kasih Bunda, Indragiri Hilir. Data were collected through observation sheets, documentation, and interviews, then analyzed using descriptive comparative analysis. The results showed a significant increase in each cycle. In the pre-cycle stage, children's average ability ranged from 30%–45%. After the implementation of Pop-Up Book media, the scores increased to 50%–68% in Cycle I and peaked at 80%–90% in Cycle II. Beyond cognitive aspects, children's learning enthusiasm also increased to 85%. It is concluded that Pop-Up Book media is effective in improving early childhood numeracy literacy by providing interactive and concrete visualizations. This media is recommended as an innovative learning tool to support holistic cognitive development in children.

Numerous research studies have been dedicated to investigating diabetic nephropathy (DN), encompassing investigations into molecular signaling and clinical outcomes. It is widely acknowledged that the pathophysiology of DN is influenced by multiple factors. Circular RNAs (circRNAs) are a type of endogenous RNAs distinguished by their covalent loop structure. The primary mechanism by which circRNAs exert their effects is through their sponging activity towards microRNAs. The involvement of circRNAs in the progression of DM has been confirmed, suggesting their potential as innovative biomarkers and therapeutic tools. In this study, a total of 75 diabetic instances were divided into 3 groups equally depending on their albuminuric state, as documented: Normo-albuminuric, Micro-albuminuric and Macro-albuminuric in addition to 25 healthy individuals as controls were involved. We measured Hsa-miR-483-3p, Hsa_circ_0123190 and Apelin receptor (APLNR) mRNA gene expressions utilizing quantitative RT-PCR. In addition, Serum levels of APLN (Apelin), Histone deacetylase 1 (HDAC1), Nuclear factor-ΚB (NF-κB), Chemoattractant protein-1 (MCP-1), PUMA, p53 upregulated modulator of apoptosis, transforming growth factor β (TGFβ) and Vascular cell adhesion molecule 1 (VCAM1) had been evaluated by ELISA technique. The work documented significantly down-regulation of Hsa_circ_0123190 and APLNR expressions in DN patients compared controls with the lowest level of expression exhibited in macroalbuminuria diabetic group. Whereas a significant up-regulation of Hsa-miR-483-3p expression had been detected in diabetic groups particularly diabetic patients with macroalbuminuria. Parallel findings had been observed concerning serum HDAC1, MCP1, NF-κB, PUMA, p53, TGFβ as well as VCAM1. The work documented the forthcoming impact of Hsa_circ_0123190 in progression of DN via its three various stages; By emphasizing its function as a dependable biomarker, this study aims to further the understanding of DN pathogenesis.

ABSTRACT Background In recent decades, emerging technologies—particularly Artificial Intelligence (AI)—have had a profound impact on the structure and decision‐making processes within educational systems. AI‐based tools now play a central role in data analysis, performance prediction, and personalised learning design. Objectives This study aimed to design and validate the Metacognitive Diagnostic Disruption in AI Decision‐Making Scale (MDD‐AI Scale) for educational administrators, focusing on metacognitive disruptions—disturbances in self‐monitoring and self‐regulation processes caused by AI involvement in decision‐making. Methods Using an exploratory mixed‐methods design, the study combined a literature review and interviews with 20 Jordanian educational administrators to develop 52 items from an initial 92. Data from 670 administrators were analysed for validity and reliability, with exploratory graph analysis (EGA) providing a network‐based perspective. Results and Conclusions Exploratory Factor Analysis (EFA) revealed a six‐factor structure—Metacognitive Self‐Monitoring Disruption, Metacognitive Self‐Evaluation Disruption, Cognitive Flexibility Reduction, Cognitive Overreliance on AI, Conflict Detection Shutdown, and Disruption of Metacognitive Decision Planning—explaining 67.6% of the variance. Confirmatory Factor Analysis (CFA) showed good fit (RMSEA = 0.072, CFI = 0.918), with AVE values &gt; 0.50 confirming convergent validity. Reliability was high across all indices ( α = 0.904–0.951; ω = 0.897–0.952; CR &gt; 0.89; ICC = 0.752–0.870). Measurement invariance across gender indicated structural equivalence. Exploratory Graph Analysis (EGA) and Random Forest Modeling (RFM) further supported robustness and predictive utility. Overall, the 42‐item MDD‐AI Scale is a valid, reliable, and innovative tool for assessing metacognitive disruptions in algorithmic decision‐making within educational management.

Advancing research on the systematic development of innovations in sustainable Product–Service Systems (PSS) is crucial, as existing studies focus primarily on isolated product- or service-based innovation methods. This lack of a holistic view limits the potential to achieve sustainability goals and competitive advantage that well-developed PSS models offer. This study addresses these issues by investigating how systematic innovation methods and tools derived from the Theory of Inventive Problem Solving (TRIZ) can resolve technical and physical contradictions throughout the life cycle of sustainable PSSs. The study’s findings demonstrate the effectiveness of systematic innovation methods and tools in supporting the development of PSS models, offering insights and mechanisms for companies to create innovative and sustainable PSS offerings. We also unveil unexplored systematic innovation strategies for PSS and propose a method that integrates TRIZ across the PSS life cycle, guiding practitioners and scholars to drive systematic innovation and enhance value creation.

ABSTRACT Large Language Models (LLMs) are increasingly integrated into co-design practices, yet their impact on collaboration structure, cognitive activity, and creativity remains underexplored. This study investigates how LLMs reshape team collaboration and cognitive processes in co-design, focusing on novice designer teams. Drawing on the co-evolution model, we propose a structured analytical framework that links collaborative cognitive activities with problem-solution structures and transitions. A mixed-methods study (N = 40) combining protocol analysis, self-report scales, and expert evaluation was conducted to investigate LLM-supported co-design. The results show that LLMs accelerate transitions between the problem and solution spaces, increasing the pace of collaboration. LLMs are also associated with fewer segments per space unit, indicating a redistribution of within-space elaboration. LLMs support information processing and early-stage ideation in the problem space. However, in the solution space, LLM use is associated with lower engagement in conceptual integration, functional reasoning, and decision-making. LLMs enhance outcome novelty and self-perceived creativity, but show limited improvement in outcome usefulness and in team-level creativity support related to expressiveness, collaboration, and immersion. These findings provide multi-level insights into how LLMs influence collaboration structure, cognitive activity distribution, and creativity in co-design, informing the targeted development of LLM-based tools for collaborative design innovation.

In developing economies like Nigeria, the need for strong collaboration among academia, industry, and government is crucial to address widespread skill gaps and promote sustainable national growth. The Students Industrial Work Experience Scheme (SIWES), created as a tripartite initiative, aims to bridge the gap between education and employment through structured industrial exposure. Although it aligns conceptually with collaborative models, the effectiveness of this mechanism as a true partnership remains debated. This qualitative study carefully examines the SIWES partnership framework by capturing the detailed perspectives of its main stakeholders. Using a purposive sampling method, in-depth semi-structured interviews were conducted with 15 key informants, including 5 institutional SIWES coordinators, 5 industry supervisors, and 5 regulatory officials from government agencies. Thematic analysis of the interview data revealed significant systemic challenges, such as fragmented stakeholder cooperation, insufficient government support, broken feedback loops, and inconsistent implementation frameworks. The results show that while SIWES is based on an important developmental idea, its actual implementation significantly diverges from the collaborative principles of the Triple Helix model. As a result, the study recommends a comprehensive reform plan, calling for holistic policy reform, stronger institutional-industry connections, better supervision and monitoring, and the use of outcome-based evaluation methods. These suggestions aim to transform SIWES into a more effective tool for workforce development and national innovation.

Artificial intelligence (AI) is increasingly transforming higher education by introducing innovative tools that enhance teaching, learning, and institutional management processes. This study presents a bibliographic review that examines the main opportunities, challenges, and ethical implications associated with the implementation of AI in higher education. The methodology involved the analysis of recent scientific literature published between 2020 and 2025 from recognized academic databases. The findings indicate that AI technologies contribute to personalized learning, intelligent tutoring systems, learning analytics, and automated assessment, improving student engagement and academic performance. However, the literature also identifies significant challenges, including limited digital competencies among educators, technological infrastructure requirements, and concerns related to academic integrity. Furthermore, the increasing use of AI raises important ethical issues such as data privacy, algorithmic bias, transparency, and accountability in educational systems. The review concludes that while artificial intelligence offers substantial potential for educational innovation, its successful integration requires responsible governance, institutional policies, and pedagogical strategies that ensure ethical and effective use in higher education environments.

Harnessing reactive oxygen species for precision medicine: ROS-Activatable PROTACs for lung Cancer.