Search Results (2,030)

Large language models (LLMs) are an emerging artificial intelligence-driven technology, based on transformer architecture. LLMs are widely used in modern education, both by learners and tutors, as standalone tools or integrated into e-learning systems, where they can support personalization, adaptive learning, automated assessment and feedback, content generation, and intelligent tutoring. LLMs offer many benefits for learners, but they also have significant limitations. One approach to address the limitations of LLMs is to combine them with other intelligent technologies. The primary goal of this systematic survey is to identify appropriate supporting technologies, mechanisms of use, and methodological approaches able to help overcome the limitations of LLMs and support their responsible and effective use in education. For this reason, analysis and discussion of recent scientific research (published over the last four years) accessible through Google Scholar, ACM, IEEE Xplore, or indexed in Scopus or Web of Science (WoS) is performed. A bibliometric analysis of results from the initial general query strings is used to refine and formulate more specific search queries during the literature retrieval process in the selected databases. Full-text exploration of relevant search results serves as a source for critical analysis and deductions leading to the following conclusion: LLMs should be integrated into e-learning systems, combined with knowledge graphs, ontologies, learning analytics, and multimodal reasoning to enhance reliability, improve pedagogical effectiveness, and enable true personalization. New pedagogical approaches are also needed to ensure the effective use of LLMs in both tutoring and assessment contexts. Therefore, the authors propose methodological guidelines for integrating LLMs in complex modular educational systems. Full article

Artificial intelligence (AI) tools are increasingly integrated into mathematics education, yet most reviews emphasize achievement rather than how AI shapes mathematical thinking. This scoping review mapped literature published between 2020 and 2026 on AI-supported mathematics learning through three cognition frameworks: APOS (Action–Process–Object–Schema), Sfard’s process–object duality and reification, and Conceptual Image theory. Searches were conducted in Scopus, Web of Science, ERIC, PsycINFO, Education Source, and IEEE Xplore, followed by duplicate removal and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR)-aligned screening. Twenty-one peer-reviewed studies met inclusion criteria (18 empirical studies plus three theoretically oriented studies). Evidence growth accelerated after 2022, with most studies situated in secondary and higher education. Large language models (LLMs) and Intelligent Tutoring Systems (ITS) were the most frequently investigated modalities. Across studies, AI commonly supported theoretically inferred action-level execution and procedural management (APOS) via adaptive feedback, hinting, and stepwise scaffolding, and it often broadened learners’ conceptual images through multiple representations and generated explanations. However, these interpretations were necessarily cautious, because very few studies directly operationalized theory-linked conceptual mechanisms such as process internalization, object encapsulation, reification, or alignment between conceptual images and formal definitions. In LLM-supported contexts, gains in explanation quality coexisted with risks of procedural outsourcing when students relied on generated solutions without prior reasoning. By contrast, ITS-based environments more often supported tightly structured procedural engagement, suggesting that different AI modalities afford different forms of cognitive support and risk. Overall, AI’s conceptual impact appears to depend less on tool availability and more on instructional orchestration (task design, prompting, and teacher mediation). The findings also suggest that sustainability-related dimensions—particularly learner agency, transparency of AI support, and equitable participation—are closely connected to whether AI use promotes durable conceptual learning rather than superficial performance gains. Future research should operationalize cognitive transitions, assess structural understanding, and report AI-use conditions transparently to support cumulative, theory-driven synthesis. Full article

Early detection remains the most decisive factor in improving outcomes for oral cancer and oral potentially malignant disorders. However, reliance on conventional biopsy-based pathways presents some practical and biological limitations. This scoping review aimed to map recent advances in non- and minimally invasive diagnostic approaches and to clarify how these innovations are being positioned within clinical workflows. Following PRISMA-ScR guidance, PubMed/MEDLINE, Scopus, and Web of Science were searched for English-language original studies published between 2020 and 2025. Two independent reviewers screened and charted data on technologies, biomarkers, sampling sources, and clinical applications. Forty-nine studies were included. The literature clustered around four main domains: enhanced cytology (including liquid-based platforms and DNA ploidy analysis), multilayer liquid biopsy strategies (miRNA, cfDNA/ctDNA, methylation panels, and autoantibodies), optical and nanotechnology-based systems (Raman/SERS and sensor platforms), and artificial intelligence-driven decision support tools. Across modalities, a shared emphasis on rapid triage, risk stratification, and follow-up monitoring was evident. Nonetheless, variability in sampling, processing, analytical thresholds, and reporting standards limited cross-study comparability. Recent innovations point toward integrated, panel-based diagnostic models. Broader clinical adoption will require methodological standardization and robust multicenter validation. Full article

Background: Transgender and gender expansive (TGE) people experience high rates of interpersonal victimization, which has been linked to high rates of post-traumatic stress disorder (PTSD, a highly disabling and under-studied mental illness among TGE people). This systematic review identifies, classifies, critically appraises, and synthesizes the peer-reviewed literature describing the association between interpersonal victimization and post-traumatic stress among TGE people. This review collates what is known about the associations between victimization and PTSD among TGE people and makes recommendations to guide future research and intervention development. Methods: Searches were conducted across five databases (PubMed, Embase, Web of Science, PsycInfo, and CINAHL) following PRISMA guidelines. Inclusion criteria were: English language; peer-reviewed original research; articles describing the association between victimization and PTSD among TGE youth or adults; reporting TGE-specific data. Exclusion criteria were: reviews, commentaries without original data, dissertations or theses, conference abstracts, animal studies, studies without TGE-specific findings, and case studies. Quality appraisal was completed for all studies, which included a discussion of bias. Data extraction was completed by two independent authors, and conflicts were resolved by a third. Data were stratified by gender identity, race or ethnicity, and type of violence for further synthesis. Results: 25 studies were evaluated for design, measure quality, and key findings. Findings were highly consistent across studies: multiple forms of interpersonal violence (e.g., childhood maltreatment, sexual violence, intimate partner violence, and transgender-specific victimization) were significantly associated with PTSD symptom severity or diagnosis across diverse identities and geographic contexts. All studies examining childhood sexual abuse reported significant associations with PTSD outcomes, highlighting early life as a critical period of vulnerability. Samples were disproportionately White and adult, with limited examination of intersectional experiences shaped by race, ethnicity, and socioeconomic status. Discussion: Interpersonal violence-related PTSD among TGE populations reflects a pervasive and systemic pattern of trauma rooted in structural discrimination rather than isolated individual risk. Addressing this inequity requires multilevel prevention and intervention strategies. Future research should prioritize longitudinal designs, culturally responsive measurement tools, and intersectional analyses to inform prevention, clinical care, and policy responses. The majority of studies were cross-sectional designs, so causality cannot be inferred. Additionally, the samples were disproportionately White and adult, which may bias the magnitude of associations reported and limit generalizability to racially and ethnically diverse TGE populations. Although many studies reported race and ethnicity descriptively, none disaggregated violence-related PTSD outcomes by racial or ethnic group within TGE samples, representing a critical limitation for intersectional analysis. Full article

Objectives: Educational attainment appears to be associated with non-alcoholic fatty liver disease (NAFLD). The inconsistent findings across existing studies necessitate a thorough meta-analysis to elucidate this association. Methods: A systematic search of PubMed, Web of Science, and Scopus was conducted from inception to 31 December 2024, without language restrictions. Data were analyzed using Review Manager 5.4, with pooled odds ratios (ORs) and 95% confidence intervals (CIs) estimated via appropriate models. Results: 27 studies involving 446,312 participants (93,116 NAFLD; 353,196 healthy individuals) were included. Noteworthy heterogeneity was detected, with I² = 96% for more-than-high-school and I² = 95% for high-school-education when we pooled all the studies together. Further subgroup analyses suggested that higher education was inversely associated with NAFLD risk in some developed countries, like the United States, while potential gender-specific effects were found among the Chinese population. Conclusions: The current meta-analysis suggests that the association between educational attainment and NAFLD is complex and context-dependent, and it may vary across different countries and types of sex. Full article

Pesticide exposure is a plausible but incompletely characterized contributor to kidney injury. This review integrates current clinical, epidemiologic, experimental, and mechanistic evidence on pesticide-related nephrotoxicity, focusing on glyphosate-based herbicides, paraquat, organophosphate insecticides, and atrazine. A structured search of PubMed and Web of Science identified English-language studies published between January 2015 and February 2026. Of 635 records screened, 61 human studies were retained for full-text evaluation, and relevant animal, in vitro, and regulatory sources were additionally reviewed for mechanistic interpretation. Across pesticide classes, the proximal tubule emerged as the most consistent renal target, although downstream pathways differed, including oxidative stress, mitochondrial dysfunction, transporter disruption, endoplasmic reticulum stress, inflammation, apoptosis, ferroptotic signaling, and fibrotic remodeling. Human evidence was strongest for acute kidney injury following severe poisoning, whereas associations between chronic occupational or environmental exposure and chronic kidney disease or end-stage renal disease were more limited and heterogeneous. Biomarkers including kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), β₂-microglobulin, cystatin C, interleukin-18 (IL-18), cytochrome c, and 8-hydroxy-2′-deoxyguanosine (8-OHdG) often detected early tubular stress before abnormalities appeared in conventional renal indices. Overall, pesticide nephrotoxicity is best conceptualized as a spectrum of mechanism-specific tubular injury signatures, supporting a shift toward biomarker-informed early detection, improved hazard identification, and more mechanistically grounded risk assessment. Full article

Background: Chronic pain in children with cancer is a major challenge in pediatric palliative care. It results from the interaction of disease-related and treatment-related factors, psychological distress, and the child’s family and social environment. When poorly controlled, it can impair quality of life, emotional development, social functioning, and family well-being. This narrative review examines the challenges and management strategies for chronic pain in children with cancer from a pediatric palliative care perspective, with attention to pain mechanisms, assessment difficulties, and psycho-emotional influences. Methods: This narrative review was based on a structured literature search conducted in PubMed/MEDLINE, Scopus, and Web of Science for English-language articles published between January 2000 and October 2025. Of 135 records identified, 15 studies judged most relevant to the thematic scope of the review were included in the final synthesis. A PRISMA-based flowchart was used to illustrate study identification and selection without implying a formal systematic review. Results: Chronic pain in children with cancer emerged as a multidimensional problem requiring an integrated approach to assessment and management, and some studies suggest that 20–26% of childhood cancer survivors experience persistent pain. Pharmacological strategies, including opioids and adjuvant medications, remain central, while psychological, supportive, and non-pharmacological interventions may complement multimodal care. Conclusions: Chronic pain in children with cancer should be managed through an integrated, individualized, and child-centered approach that addresses the physical, emotional, social, and relational dimensions of suffering and may improve quality of life for both children and their families. Full article

Background: Medial-compartment knee osteoarthritis (KOA) carries substantial disability and long-term cost. High tibial osteotomy (HTO) and unicompartmental knee arthroplasty (UKA) are key joint-preserving or joint-replacing options for selected patients, but their comparative economic ranking remains uncertain. Methods: This critical narrative review synthesized comparative economic evidence on HTO versus UKA for isolated medial-compartment KOA. PubMed and Web of Science were searched as primary sources for English-language studies published from 1 January 2000 to 15 January 2026, while Google Scholar and citation tracking were used supplementarily to identify potentially missed records. Eligible studies were direct economic evaluations or comparative cost/resource studies with clear decision relevance to the HTO–UKA choice. Burden and cost-of-illness studies were used for contextual framing only and were not included in the core comparative synthesis. Results: The direct evidence base was small and methodologically heterogeneous and was dominated by decision-analytic models that differed in perspective, time horizon, utility metric, and assumptions regarding reoperation, revision, and conversion to total knee arthroplasty (TKA). These structural differences largely explain why a U.S. lifetime societal model favored HTO, a UK age-stratified 10-year model produced age-dependent findings, and a recent Canadian public-payer model favored UKA. Observational studies suggest that UKA episode costs can fall substantially in outpatient or ambulatory pathways, whereas HTO costs may rise when reoperations and technique-specific resource use are explicitly captured. Conclusions: Current evidence does not support a context-free economic ranking of HTO and UKA. Because the available studies are heterogeneous and incremental utility differences are often small, the findings should be interpreted cautiously and as scenario-dependent rather than definitive. Future comparative analyses should use contemporary pathway data, transparent and standardized costing, and explicit downstream event definitions for both procedures. Full article

Objectives: The aim of this systematic review was to evaluate whether artificial intelligence systems improve the diagnosis and localization assessment of impacted canines in radiological imaging. Methods: A systematic literature search was conducted across four electronic databases (MEDLINE/PubMed, Scopus, Embase, and Web of Science) for studies published after 2020, with no language restrictions. Eligible studies were comparative studies involving human subjects that evaluated AI-based systems against experienced clinicians or accepted radiological reference standards for the detection and localization of impacted canines. The risk of bias and applicability were assessed using the adapted QUADAS-3 tool. The review protocol was prospectively registered in PROSPERO (CRD42023487320). Results: The search strategy identified 110 records. After the removal of 41 duplicates, 69 articles were screened by title and abstract. Seventeen studies underwent full-text evaluation, and eight studies met the inclusion criteria and were included in the qualitative synthesis. Across the included studies, the overall risk of bias was considered high, primarily due to retrospective study design and limitations in reporting of methodological procedures. Conclusions: The available evidence does not provide high-quality studies addressing the studied issue. AI appears to yield more favorable results in CBCT analysis when compared to panoramic radiographs. However, this observation should be interpreted with caution, because the compared studies did not address the same clinical task, since these radiographs were taken in different clinical situations. Further well-designed studies with standardized datasets and external validation are required to better define the potential of artificial intelligence in orthodontic radiological diagnostics. Full article

Currently, Large Language Models (LLMs), exemplified by ChatGPT, are accelerating technological development across various domains, including the environmental domain, owing to their powerful text-generation and information-processing capabilities. With changes in global climate and environmental conditions, environmental sustainability has emerged as a major global challenge. Leveraging LLMs to advance environmental sustainability and mitigate current environmental problems is considered a valuable and effective approach. This study aims to systematically synthesize research progress and core challenges in current LLMs for promoting sustainability-related fields, and to comprehensively analyze the application contexts, impacts, and development potential of various LLMs within the environmental sector. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across six databases: Web of Science (WOS), Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. A total of 20 articles were ultimately included for analysis. The findings indicate that LLMs play a positive role in maintaining environmental sustainability and promoting the low-carbon energy transition. The applications of LLMs span six core domains: the green transition, carbon emission management, air quality assessment, smart city operations, map analysis, and human cognition and behavioral observation. However, the training and operation of current LLMs consume considerable resources, which creates an inherent conflict with the goals of sustainable development. Future efforts must focus on developing a secure, equitable, and scalable LLM support system to advance environmental sustainability. This requires optimizing model energy efficiency and ensuring a balance between performance, reliability, and environmental impact. These endeavors are crucial for addressing environmental problems and guaranteeing the sustainable progression of LLMs across diverse environmental contexts. Full article

Background/Objectives: Financial toxicity (FT) is increasingly recognised as a critical dimension of the cancer care continuum, reflecting both objective financial burden and subjective financial distress arising from cancer-related care. Head and neck cancers (HNC) may be particularly vulnerable to FT because treatment often involves multimodal care, functional morbidity, prolonged rehabilitation, and disruption to employment. This scoping review mapped and synthesised the literature on FT in a focused subset of head and neck cancers (HNC), namely malignancies of the oral cavity, oropharynx, nasopharynx, sinonasal tract, and major and minor salivary glands. Methods: A scoping review was conducted in accordance with the methodological guidance of the Joanna Briggs Institute for scoping reviews to identify and synthesise studies addressing FT in the selected HNC subsites. Searches were undertaken in MEDLINE, Embase, Scopus, Web of Science, CINAHL, EconLit, and Global Index Medicus for English-language studies published between 1 January 2015 and 1 January 2025. The search window was restricted to this period to capture the more contemporary evolution of FT as a distinct research construct in oncology. Eligible studies included adult patients and reported patient-level FT outcomes, including direct costs, indirect costs, out-of-pocket expenditure, financial hardship, financial distress, employment disruption, or related economic strain. Findings were synthesised narratively and organised thematically. Results: Twenty-five studies published between 2015 and 2025 were included. The evidence base was dominated by cross-sectional and retrospective designs, with limited prospective follow-up and very little intervention-focused research. FT was conceptualised heterogeneously across studies, spanning direct expenditure, indirect and non-medical costs, subjective financial distress, and coping-related consequences. Questionnaire-based approaches were used in 13 studies, but only a smaller subset employed FT-specific instruments such as COST. Across the literature, FT was most commonly associated with lower income, weaker financial protection, employment disruption, rural residence in some settings, and more intensive treatment. Reported downstream associations included poorer quality of life, psychological distress, care alteration, and work-related burden, although evidence for treatment delay or survival effects was more limited and should be interpreted cautiously. Conclusions: In this focused HNC subset, FT appears multidimensional, socially patterned, and clinically relevant. However, the literature remains methodologically fragmented, with inconsistent measurement and sparse longitudinal evidence. Future work should prioritise validated and tumour-specific assessment strategies, prospective study designs, and evaluation of mitigation interventions that address both direct and indirect burden across the cancer continuum. Full article

Sensorineural hearing loss (SNHL) can result from genetic mutations, excessive noise exposure, ototoxic drugs, and aging. Glutamate excitotoxicity is one of the underlying mechanisms of SNHL. However, the specific roles of different glutamate receptor subtypes in normal signaling and excitotoxic damage remain unclear. Addressing these questions requires relevant experimental models. This review compares existing protocols for the isolation and cultivation of primary spiral ganglion cells. It also evaluates the utility of this model for studying glutamatergic transmission and glutamate-induced excitotoxicity. A literature search was conducted in PubMed, Scopus, Google Scholar, and Web of Science. We identified 16 relevant English-language articles published since 1990, when the model was first used to study glutamatergic signaling. Our analysis reveals significant heterogeneity in spiral ganglion cell isolation protocols and culture conditions. We highlight major differences in glutamate concentrations and exposure times used to model excitotoxicity. The most significant limitation of this model is the loss of the native microenvironment of auditory neurons, including their dendritic and axonal contacts. Nevertheless, primary spiral ganglion cells serve as a suitable in vitro model for investigating auditory neuron function and pathology. The number of neurons and neurite length serve as reliable indicators of otoprotective effects under conditions of glutamate excitotoxicity. Based on an analysis of the key stages of primary SGC culture establishment, this study proposes approaches to overcome limitations and improve the practice of using this model. A better understanding of the function of glutamate receptors of SGNs and the mechanisms behind glutamate excitotoxicity could help us to develop new treatments for SNHL. This review serves as a practical guide for researchers implementing or optimizing primary SGC cultures. Full article

Objective: This narrative review synthesizes current evidence on materials and manufacturing technologies for customized dental implants, highlighting their comparative advantages and limitations. Methods: A structured literature search (December 2024–January 2025) was conducted using PubMed, Web of Science, Scopus, and Google Scholar. Peer-reviewed English-language articles (mainly 2015–2025) addressing implant materials, manufacturing methods, and surface modifications were included. Data were critically analyzed and thematically organized without meta-analysis. Results: Digital workflows are advancing implantology toward patient-specific solutions. Subtractive manufacturing (SM) ensures high precision and surface quality but is limited by material waste and geometric constraints. In contrast, additive manufacturing (AM) enables complex, porous, and customized designs, though often requires post-processing. Titanium and its alloys remain the gold standard due to strength and biocompatibility, while TiZr and β-type alloys may reduce stress shielding. Zirconia offers aesthetic benefits but is brittle, whereas PEEK shows favorable elasticity but limited bioactivity. Surface modifications enhance osseointegration and long-term performance. Conclusions: Combining digital workflows with SM and AM supports development of optimized, patient-specific implants. While titanium dominates clinical use, emerging materials offer specific advantages. Further clinical validation and standardization are required. Full article

Over the past two decades, Citizen Science (CS) has experienced rapid growth, driven by technological advancements and the rise of digital platforms. This work examines the necessity for standardization in Citizen Science data management and discusses how existing data standards can enhance the impact of citizen-generated data. CS standardization ensures data quality, comparability, reusability, and interoperability, making data suitable for contributing to the Marine Strategy Framework Directive (MSFD) and the United Nations Sustainable Development Goals (SDGs). This paper examined 130 Citizen Science publications and found that most collected data referred to the MSFD Descriptor 1 (Biodiversity—44.96%) and Descriptor 10 (Marine Litter—20.93%), followed by the alien species distribution (D2—11.63%), hydrography (D7—6.20%), eutrophication (D5—6.20%), and marine pollution (D8—3.10%). Analysis of 108 publications on SDG alignment revealed that the majority (35.58%) focused on reducing marine pollution. This paper reviews the best practices for effective Citizen Science data management, including standards for data structures, content, values, and exchange. Based on this review, Darwin Core, Ecological Metadata Language (EML), and the OGC SensorThings API appear to be the most suitable standards for MSFD-relevant CS data. Therefore, policymakers could enable the formal integration of standardized CS datasets into MSFD monitoring workflows. Full article

Vanilla (Vanilla planifolia Jacks. ex Andrews) is a tropical orchid of high economic value, with an annual production of 8000 to 10,000 t and a market exceeding 800 million USD in over 40 countries. In vitro propagation has strengthened the innovation, production, and conservation of this species. Bibliometrics, as a quantitative approach, systematically examines the patterns, dynamics, and evolutionary trends of scientific production. A systematic search was conducted in Scopus and Web of Science until December 2025, using the terms “vanilla” and “micropropagation”. A total of 53 documents were identified in Scopus (1997–2025) and 39 in Web of Science (2000–2025). The evaluated indicators included: year of publication, country of origin, language, areas, main categories, document typology, authorship, and keyword distribution. VOSviewer was used for keyword analysis to identify author collaboration networks and emerging trends. The years with the most information were 2024 and 2025, with Mexico and India standing out prominently. The main thematic areas were Agricultural and Biological Sciences, and the role of researcher Ramírez-Mosqueda was highlighted. The keywords with the highest correlation and impact were bioreactors, vanillin, and cryopreservation. This bibliometric study provides a comprehensive perspective on scientific production related to vanilla micropropagation. The results highlight the multidisciplinary nature of biotechnology applied to this crop, integrating contributions from various areas of knowledge for the benefit of the main actors in the value chain. Full article