Search Results (1,822)

Generative Artificial Intelligence (GenAI) is reshaping how students engage with learning both within and beyond traditional classroom settings. In a time when the development of transferable skills is essential for enabling students to thrive in varied and rapidly evolving environments, the potential of GenAI to enhance learning engagement remains insufficiently understood. Despite rising interest in interactive, personalised learning companions that enable deep engagement and ongoing skills development, scholarly research remains limited. This gap constrains effective institutional use of GenAI, reinforces black-box thinking, and restricts understanding of meaningful student engagement and skills acquisition. This paper investigates how a GenAI-enabled AI teacher supports student learning engagement, focusing on behavioral engagement as evidenced by learner interaction and participation patterns across diverse international higher education institutions. Using a combination of quantitative engagement metrics and qualitative learner reflections, the study examines how GenAI supports personalised learning, sustained interaction, autonomy, and cognitive engagement among students with varying educational backgrounds. The findings demonstrate that GenAI-based teaching systems can promote meaningful learning engagement, enhance motivation, and strengthen the development of transferable and employability skills. The study contributes empirical evidence to current debates on GenAI integration, teacher practices, and student engagement, offering implications for curriculum design and institutional adoption of GenAI-enabled learning tools. Full article

Background/Objectives: Blood glucose prediction (BGP) for individuals with type 1 diabetes (T1D) is a clinically essential yet highly challenging task in time series forecasting (TSF) and an important problem in personalised medicine. Accurate bespoke BGP is crucial for individualised T1D management, reducing complications, and supporting patient-specific glycaemic risk mitigation. However, the pronounced volatility of glycaemic fluctuations in T1D, combined with the need for mathematical rigor and clinical relevance, hampers reliable prediction. This complexity underscores the demand to explore and enhance more advanced techniques. While adversarial learning is adept at modelling intricate data variability, its potential for BGP remains largely untapped. Methods: This work presents a novel approach for BGP by addressing a key limitation in conventional adversarial learning when applied to this task. Typically, these methods optimise prediction accuracy within a set horizon by minimising adversarial loss. This focus overlooks how predictions align with longer-term patterns, which are critical for clinical relevance in BGP, thereby yielding suboptimal results. To overcome this limitation, we introduce collaborative augmented adversarial learning, designed to improve the model’s temporal awareness. Incorporating collaborative interaction optimisation, this approach enables the model to reflect extended time dependencies beyond the immediate horizon, thereby improving both the clinical reliability of predictions and overall predictive performance. We develop and evaluate four learning systems for BGP: independent learning, adversarial learning, collaborative learning, and adversarial collaborative learning. The proposed systems were evaluated for two clinically relevant prediction horizons, namely 30 min and 60 min ahead. Results: The interdependent collaboratively augmented learning frameworks, validated using the well-established Ohio T1D datasets, demonstrate statistically significant superior performance in both clinical and mathematical evaluations. Conclusions: Beyond advancing BGP accuracy and clinical reliability, the proposed approach supports personalised medicine by improving subject-specific glucose forecasting from CGM data, with potential relevance for more individualised diabetes monitoring and decision support. The proposed approach also opens new avenues for advancements in other complex TSF domains, as outlined in our future work. Full article

Background: Current guidelines acknowledge that early discharge is not associated with late mortality and that in-hospital length of stay (LOS) of 48–72 h should be considered following successful primary percutaneous coronary intervention (PPCI) in low-risk patients. Recent studies have highlighted the safety of very early discharge after PPCI in highly selected low-risk patients; however, objective tools to guide discharge timing remain limited. The Index of Microcirculatory Resistance (IMR) offers a quantitative assessment of microvascular function and may help identify patients suitable for very early discharge. We aimed to evaluate the feasibility of using IMR to guide very early discharge in patients who underwent uncomplicated PPCI. Study design and objectives: The Safety of Early Discharge Using Index Microcirculatory Resistance in Patients with Acute Myocardial Infarction (SECURE) study is designed to assess the feasibility of using IMR, measured immediately following successful PPCI, to guide early discharge from hospital within 24 h. The SECURE study is a prospective, proof-of-concept, functional non-inferiority, single-centre, randomised, open-label trial to determine if patients with low IMR can be safely discharged when compared to standard discharge policy. The SECURE study will recruit 82 patients with low IMR following successful PPCI. Participants will be 1:1 randomised to either standard discharge timing or very early discharge (within 24 h). The left ventricle ejection fraction will be assessed using cardiac magnetic resonance imaging. A telephone follow-up at 3 months will be arranged. Clinical events are collected as secondary and exploratory safety endpoints. Conclusions: The SECURE study will provide proof-of-concept data about the feasibility of using IMR to guide very early discharge following PPCI. If successful, this study will provide data to plan for a larger study to determine the safety of this personalised approach. Full article

This paper presents a low-cost, lightweight wearable sensing module for real-time multi-degree-of-freedom motion analysis, which is validated using ankle movements from a representative case study. The system is based on a compact inertial measurement unit integrated into a custom-made enclosure and employs Kalman filter-based sensor fusion to estimate three-dimensional joint orientation. An experimental campaign involving sixteen healthy participants was conducted, and measurements were compared against a gold-standard optical motion capture system, Optitrack V120 Trio. Ankle kinematics were analysed across all anatomical planes, including dorsiflexion/plantarflexion, inversion/eversion, and adduction/abduction. Quantitative metrics, including cosine similarity consistently above 0.98 across all movements and root mean square error within 4° on average, demonstrate strong agreement between the angular measuring device and motion capture data, with errors remaining within clinically acceptable limits. The results confirm the feasibility of the proposed system as a reliable, portable, and affordable alternative to laboratory-based measurement technologies. Beyond ankle assessment, the sensing approach is applicable to a wide range of motion-assistive and rehabilitation systems, supporting continuous monitoring, personalised therapy, and future integration into intelligent wearable devices. Full article

The increasing integration of Generative AI (GenAI) into higher education, particularly in the domain of language teaching, presents both opportunities and challenges. While AI-powered tools such as ChatGPT-5 can support language learning by generating personalised content which enables real-time interaction and feedback, they also risk perpetuating biases embedded in training data. These biases can appear in linguistic, cultural or socio-political forms, reinforcing stereotypes and influencing language norms. Therefore, equipping students and educators with strategies to critically assess AI outputs is essential for ethical and responsible AI use in language education. While recent research highlights the risks of algorithmic bias, less attention has been given to the perceptions and attitudes of pre-service teachers, whose future practice will shape classroom uses of these technologies. This exploratory pilot study adopts a survey-based approach to examine pre-service teachers’ baseline awareness of bias in artificial intelligence, with particular attention to linguistic and cultural dimensions Data were collected through an online questionnaire administered to 65 undergraduate students enrolled in Primary Education degree programmes. The study documents baseline perceptions prior to any instructional intervention and provides preliminary empirical evidence to inform the future design of pedagogical strategies aimed at developing critical AI literacy in teacher education. Full article

Students’ use of generative artificial intelligence (GAI) to avoid engaging in generative processing can undermine the validity of higher education. In contrast, Flipped-Interaction Intelligent Tutoring Systems (FIITSs) may promote active engagement by leading a personalised dialogue. The underutilisation of FIITS may stem from the lack of a framework to guide prompt creation and from a dearth of published FIITS prompt examples. This article presents the Flipped-Interaction Prompt (FIP) Framework, abstracted from two validated prompts. To achieve this validation, 26 preservice science education students at a South African university engaged with either prompt in a free GAI five times over ten weeks. The resulting 114 engagements, each involving at least 10 flipped-interaction dialogue exchanges, were analysed for implementation fidelity and for students’ engagement in generative processing. Findings were triangulated against questionnaire and group interview responses, as well as written reflections. The technical implementation was closely aligned with the prompt instruction, with minor deviations noted for not providing answers outright. Additionally, students demonstrated moderate to high levels of generative processing. Findings support the efficacy of the abstracted FIP Framework in guiding the creation of FIITS prompts. Investigating instantiations for additional subject domains would further strengthen confidence in this framework. Full article

Background: Childhood vaccination remains the cornerstone of public health strategies, substantially reducing global morbidity and mortality, yet suboptimal uptake persists in many settings. In South Africa, the challenge is evident, with persistent outbreaks of vaccine-preventable diseases. Addressing localised immunisation shortfalls requires elucidating the complex interplay of factors beyond conventional access barriers. This qualitative study provides context-specific insights into the behavioural and social drivers influencing childhood vaccination uptake among caregivers in Cape Town, South Africa. Methods: Utilising an exploratory qualitative research design, thematic analysis was applied to interview data (n = 25 caregivers) collected via a purposive sampling strategy designed to capture maximum variation in experiences within targeted low-uptake subdistricts. Interpretation of the data was systematically guided by the World Health Organization’s Behavioural and Social Drivers (BeSD) framework. The latter consists of four domains, namely, “Thinking and Feeling”, “Social Processes”, “Motivation”, and “Practical Factors”. Findings: Analysis across BeSD domains reflected a pattern of the intention–behaviour gap, where caregivers are motivated for vaccination but face structural and practical barriers affecting timely uptake. In the Thinking and Feeling domain, widespread conviction regarding the vital benefits of vaccination co-existed with significant anxiety concerning minor side effects (e.g., pain and fever), which sometimes precipitated missed subsequent appointments. Caregivers frequently accept immunisation as a social routine despite having limited knowledge of the diseases it prevents. Social Processes demonstrated that while decision-making authority rested primarily with mothers, compliance relied on the delegation of logistical responsibilities to extended family members. Critically, reports of poor communication, judgment, or negative attitudes among healthcare workers undermined trust and acted as barriers to sustained engagement. Within the Practical Factors domain, structural constraints frequently overshadowed high intent, with pervasive issues such as long waiting times and financial costs cited as the main reasons for missed appointments. Conclusions: Participants generally expressed strong acceptance of vaccination, but attainment of optimal coverage is constrained by systemic failures in patient–provider communication and persistent logistical barriers within the public healthcare delivery system. Strategic public health interventions must therefore move beyond addressing only attitudinal opposition to prioritise targeted efforts that mitigate structural constraints and reinforce personalised, empathetic communication to sustain caregiver confidence and adherence. Full article

Artificial intelligence (AI) in cardiovascular pathology involves the use of computational models, including machine learning and deep learning (DL), to analyse complex and heterogeneous data. These data include histopathological whole-slide images, cardiovascular imaging techniques such as cardiac magnetic resonance, echocardiography, computed tomography (CT), clinical parameters, and molecular information. The integration of these multimodal data sources allows AI to overcome the limitations of single-modality analysis, improving diagnostic accuracy, prognostic stratification, and personalised clinical decision-making while reducing inter-observer variability. Cardiovascular disease remains the leading cause of mortality worldwide, highlighting the need for more precise and timely diagnostic tools. AI has shown significant promise, particularly in digital pathology, where the digitisation of histological slides combined with advanced algorithms enables improved diagnosis, prognostic assessment, and translational research. This review summarises current AI applications in cardiovascular pathology, focusing on heart transplant rejection, cardiomyopathies, myocarditis, and atherosclerotic and valvular diseases. Automated methods offer important advantages, including diagnostic standardisation, quantitative histological analysis, and improved reproducibility. However, several challenges remain, such as the need for large, well-annotated shared datasets, limited interpretability of AI models, and ethical and legal issues related to clinical implementation. AI represents a promising tool for advancing cardiovascular pathology and personalised medicine, although robust multicentre validation is required before routine clinical adoption. Full article

Background: Sustaining long-term lifestyle change remains a major challenge in preventive health. Epigenetic clocks offer a dynamic, modifiable measure of biological ageing that may enhance motivation when returned to individuals. Objectives: This study had two aims: (1) to evaluate whether personalised health reports integrating epigenetic age, polygenic cancer risk scores, and lifestyle metrics could motivate sustained behavioural change; and (2) to examine variability across epigenetic clock generations to inform the selection of a suitable model for participant feedback. Methods: A total of 178 adults were recruited via the Graham Fulford Charitable Trust community testing programme, and 91 completed a one-year follow-up survey assessing behavioural, psychological, and knowledge-related outcomes. DNA methylation data from 140 samples were used to compare 14 epigenetic clocks across four generations. Results: Most participants reported positive lifestyle changes, including feeling healthier (72.5%), increased physical activity (60.4%), and improved diet (47.3%). Gains were also observed in health knowledge (63.7%) and psychological well-being (31.9%). Epigenetic clock comparisons revealed substantial heterogeneity across models. Zhang2019-BLUP was selected as a stable and interpretable measure of biological age that can be readily communicated to participants, supporting empowerment and improved health literacy, rather than serving only as a risk prediction metric. Conclusions: Personalised biomarker feedback including epigenetic age combined with lifestyle and wearable data can support self-reported improvements in health-related behaviours. Community-based delivery through trusted local networks proved effective. The marked variation between epigenetic clocks highlights the importance of selecting models designed for clear communication when used in public-facing health interventions. Full article

This review evaluates the emerging role of circulating tumour cells (CTCs) as clinically meaningful, minimally invasive biomarkers for oral squamous cell carcinoma (OSCC). Despite advances in management, OSCC continues to demonstrate high morbidity and mortality, largely due to late diagnosis and the absence of validated biomarkers for early detection or real-time monitoring. Conventional diagnostic tools, tissue biopsy, and imaging provide only static snapshots and fail to capture tumour heterogeneity or evolving biological behaviour. CTCs offer a novel and significant opportunity to address these limitations. Key findings from recent studies highlight that CTC enumeration correlates with tumour burden, nodal metastasis, recurrence, and overall prognosis. Molecular and phenotypic characterisation further reveals dynamic traits such as epithelial–mesenchymal transition, stemness, and therapy resistance, providing insights into metastatic potential and treatment failure. Technological advances, including immunocytochemistry, microfluidic capture platforms, PCR-based assays, and next-generation sequencing, have enhanced the sensitivity and specificity of CTC detection and enabled detailed multi-omic profiling. Collectively, evidence suggests that integrating CTC analysis into OSCC clinical workflows could improve early detection, refine risk stratification, personalise therapeutic strategies, and support longitudinal monitoring of disease dynamics. As research progresses, CTC-based diagnostics represent a promising frontier in shifting OSCC management toward more precise, adaptive, and biologically informed care. Full article

Background/Objectives: For patients with Ulcerative Colitis (UC) requiring surgical treatment, post-operative dietary management can pose significant challenges. TikTok is emerging as a popular social media platform for dissemination of health and nutrition information. The aim of this study is to analyse patient-generated content on TikTok regarding dietary management post-UC surgery, in order to identify recurring themes and highlight patient priorities. Methods: Relevant TikTok videos were identified through a systematic search. Search terms were developed by combining ‘diet UC’ or ‘nutrition UC’ with common UC surgical procedures. From each search term, the first 10 videos were screened. If a search produced fewer than 10 results, all identified videos were retrieved. Inclusion criteria were videos in English, and a strong indication that the content creator was diagnosed with UC and had undergone relevant surgery, and was providing nutrition recommendations. Thematic analysis of video transcripts was conducted using Braun and Clarke’s framework to identify common themes. Results: A total of 89 videos, created between 2021 and 2024, were found on the initial search, of which 12 duplicates were removed, and 77 videos were screened. Sixteen English language videos met the inclusion criteria and were analysed. Thematic analysis identified three overarching themes: (1) adaptive dietary progression in the post-surgical period, where patients described a phased approach to reintroducing foods post-surgery; (2) personalisation of diet, highlighting individualised strategies for symptom and hydration management; and (3) Emotional and social impact of dietary restrictions and modifications, including fear of food and social isolation. Conclusions: This thematic analysis offers an insight into how patients navigate the complex management of diet following UC surgery. It is important for clinicians to discuss the dietary information and online content patients are exposed to in relation to their condition. Additionally, clinical practice should evolve to embrace patient-centred, multidisciplinary approaches that validate lived experience, ensure consistent dietary guidance, and address the psychological burden of dietary restriction. Full article

Industry 5.0 (I5.0) reframes industrial transformation by placing human-centricity, sustainability, and resilience alongside digitalisation, and by linking the twin transition to circular economy ambitions. While the post-2020 literature is expanding, implications for Manufacturing Systems are presented as fragmented principles, technologies, or isolated use cases, which complicates traceability from I5.0 goals to system-level requirements. This manuscript addresses this gap by consolidating the I5.0 discourse via a challenge-based synthesis and translating it into Manufacturing System implications using an evidence-mapping logic. Reported challenges are clustered into four topic groups (planet and society, products and consumption, production, people) and mapped to the four Manufacturing System pillars to expose evidence concentrations and gaps. Building on this bridge, a Manufacturing Systems’ challenges taxonomy is derived in three streams: (i) personalised and circular products, (ii) sustainable, flexible, human-centric Manufacturing Systems, and (iii) an education and skills paradigm for reskilling across industry and research ecosystems. A research agenda matrix highlights priorities in lifecycle information infrastructures, orchestration metrics, human–automation symbiosis, and governance at a system-of-systems scale. In the coded corpus (n = 30), evidence is denser in Manufacturing Systems and operations and competitiveness and people (22 and 23 papers) than in materials and processes and product, tooling, and assembly (7 and 10 papers). Full article

Neurodegenerative diseases (NDs) are increasingly considered neurometabolic disorders driven by early mitochondrial dysfunction, neuroinflammation, and synaptic alterations that precede clinical symptoms. This review summarises pre-clinical and experimental evidence suggesting that intermittent fasting (IF) may influence these early pathogenic processes by promoting metabolic switching, enhancing autophagy and mitochondrial quality control, and modulating neuroimmune pathways. We discuss recent advances in biomarker research supporting the early detection of neurodegenerative changes, including ultrasensitive analytical platforms that can identify neuronal, glial, and synaptic injury during preclinical stages. By integrating these biomarker developments with findings from human and experimental intermittent fasting studies, we highlight how high-sensitivity assays provide quantifiable insights into the neurometabolic effects of fasting. Furthermore, we discuss how precision nutrition strategies incorporating multimarker panels, phenotypic and epigenetic signatures, and longitudinal multi-omics profiling may facilitate personalised intermittent fasting protocols and improve monitoring of biological responses. Overall, these findings underscore the relevance of a clinical biochemistry perspective integrating advanced biomarker technologies to evaluate the neurometabolic effects of intermittent fasting as a potential early neuroprotective strategy for individuals at risk of neurodegeneration. Full article

Background and Objectives: Despite critically low levels of chronic kidney disease (CKD) awareness worldwide, there is no psychometrically validated instrument to comprehensively assess CKD awareness across socioecological levels. This study aimed to develop, psychometrically evaluate and validate a multidimensional awareness scale grounded in socioecological theory for patients with stage 3–4 CKD. Materials and Methods: This methodological study enrolled 908 stage 3–4 CKD patients. Scale development proceeded through systematic stages: comprehensive literature review, qualitative interviews (n = 15), expert panel evaluation (n = 25), and pilot testing. The initial 72-item pool was refined to 41 items (Content Validity Index = 0.912). The sample was randomly split for exploratory factor analysis (EFA; n = 454) and confirmatory factor analysis (CFA; n = 454). Psychometric evaluation encompassed internal consistency (Cronbach’s α, McDonald’s ω), test–retest reliability (n = 30; 4-week interval), convergent validity (average variance extracted [AVE], composite reliability [CR]), discriminant validity (Fornell–Larcker criterion), and criterion validity (correlation with Turkish Health Literacy Scale-32 [TSOY-32]). Results: EFA revealed a seven-factor structure with an acceptable explained variance of 43.8%. Following iterative item elimination based on communalities (h² < 0.20) and factor loadings (λ < 0.30), CFA confirmed the final 34-item model with good fit (CFI = 0.972; RMSEA = 0.070 [90% CI: 0.067–0.074]). The factor structure captured awareness across core socioecological levels (Individual, Interpersonal/Institutional, Community, and Systemic), complemented by Treatment Adherence and Social Impact dimensions. Internal consistency coefficients were α = 0.884 and ω = 0.889 for the total scale. Test–retest reliability yielded an ICC of 0.954 (95% CI: 0.907–0.978). Convergent and discriminant validity were confirmed via composite reliability (CR: 0.740–0.953) and the Fornell–Larcker criterion. Criterion validity analysis revealed a significant correlation with TSOY-32 (r = 0.810, p < 0.001). Conclusions: The CKD Awareness Scale (CKD-AS-34) represents a novel, psychometrically validated, multidimensional awareness instrument for CKD. This scale enables clinicians to identify awareness deficits spanning individual to systemic levels, facilitating personalised patient education and targeted public health interventions. Full article

Background/Objectives: Breast cancer remains the leading cause of cancer-related death among women worldwide. While tools such as Adjuvant Online, PREDICT, OncotypeDx and Mammoprint identify patients at higher risk of relapse who should therefore be offered chemotherapy, there are currently no tools to accurately predict response to chemotherapy, with varied response rates (regardless of subtypes, etc.) of 8–70% reported. Accurately stratifying patients based on their likelihood of benefiting from SoC chemotherapy is therefore critical to guide personalised treatment decisions. Methods: A retrospective cohort of 293 breast cancer patients treated with SoC adjuvant anthracycline-based regimen was analysed. Five biomarkers (TOP2A, PTEN, EGFR, IGF1R, and phospho-mTOR), selected for their prognostic and therapeutic relevance, were assessed using immunohistochemistry (IHC) combined with digital pathology. Results: Biomarker expression was quantified using the digital pathology platform, QuPath, with each marker, when stratified based on high/low expression, demonstrating a significant association with relapse-free survival following SoC chemotherapy in specific subtypes of breast cancer. A composite five-biomarker signature was then generated by integrating the individual biomarker scores to improve prognostic precision. Patients with a five-biomarker signature score greater than zero exhibited a significantly higher likelihood of favourable outcomes following anthracycline-based chemotherapy compared with those with a score of zero or below. Conclusions: This study establishes a novel IHC-based five-biomarker signature capable of predicting patient outcome in the context of SoC chemotherapy. As the signature relies exclusively on IHC, it is simple, cost-effective and readily integratable into routine diagnostic workflows. In addition to its prognostic value, several biomarkers within the panel are potentially actionable, offering opportunities to guide targeted therapies in patients predicted to have poor response to conventional chemotherapy. Full article