Search Results (123)

Background/Objectives: Multifactorial prevention of falls in persons with dementia has minimal or non-significant effects. Personalised prevention is recommended. We have previously shown that gait speed, basic activities of daily living (ADL), and depression (high Cornell scores) were independent predictors of falls in persons with mild and moderate cognitive impairment. This study explored person-specific risks of falls related to physical, mental, and cognitive functions and types of dementia: Alzheimer’s disease (AD), vascular dementia (VD), mixed Alzheimer’s disease/vascular dementia (MixADVD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB). Methods: The study used data from “The Norwegian Registry of Persons Assessed for Cognitive Symptoms” (NorCog). Differences between the dementia groups and predictors of falls, gait speed, ADL, and Cornell scores were analysed. Results: Among study participants, 537/1321 (40.7%) reported a fall in the past year, with significant variations between dementia diagnoses. Fall incidence increased with age, comorbidity/polypharmacy, depression, and MAYO fluctuation score and with reduced physical activity, gait speed, and ADL. Persons with VD and MixADVD had high fall incidences and impaired gait speed and ADL. Training of physical fitness, endurance, muscular strength, coordination, and balance and optimising treatment of comorbidities and medication enhance gait speed. Improving ADL necessitates, in addition, relief of cognitive impairment and fluctuations. Relief of depression and fluctuations by psychological and pharmacological interventions is necessary to reduce the high fall risk in persons with DLB. Conclusions: The fall incidence and fall predictors varied significantly. Personalised interventions presuppose knowledge of each individual’s fall risk factors. Full article

Background: This study presents an innovative approach to cardiovascular disease (CVD) risk prediction based on a comprehensive analysis of clinical, immunological and biochemical markers using mathematical modelling and machine learning methods. Baseline data include indices of humoral and cellular immunity (CD59, CD16, IL-10, CD14, CD19, CD8, CD4, etc.), cytokines and markers of cardiovascular disease, inflammatory markers (TNF, GM-CSF, CRP), growth and angiogenesis factors (VEGF, PGF), proteins involved in apoptosis and cytotoxicity (perforin, CD95), as well as indices of liver function, kidney function, oxidative stress and heart failure (albumin, cystatin C, N-terminal pro B-type natriuretic peptide (NT-proBNP), superoxide dismutase (SOD), C-reactive protein (CRP), cholinesterase (ChE), cholesterol, and glomerular filtration rate (GFR)). Clinical and behavioural risk factors were also considered: arterial hypertension (AH), previous myocardial infarction (PICS), aortocoronary bypass surgery (CABG) and/or stenting, coronary heart disease (CHD), atrial fibrillation (AF), atrioventricular block (AB block), and diabetes mellitus (DM), as well as lifestyle (smoking, alcohol consumption, physical activity level), education, and body mass index (BMI). Methods: The study included 52 patients aged 65 years and older. Based on the clinical, biochemical and immunological data obtained, a model for predicting the risk of premature cardiovascular aging was developed using mathematical modelling and machine learning methods. The aim of the study was to develop a predictive model allowing for the early detection of predisposition to the development of CVDs and their complications. Numerical methods of mathematical modelling, including Runge–Kutta, Adams–Bashforth and backward-directed Euler methods, were used to solve the prediction problem, which made it possible to describe the dynamics of changes in biomarkers and patients’ condition over time with high accuracy. Results: HLA-DR (50%), CD14 (41%) and CD16 (38%) showed the highest association with aging processes. BMI was correlated with placental growth factor (37%). The glomerular filtration rate was positively associated with physical activity (47%), whereas SOD activity was negatively correlated with it (48%), reflecting a decline in antioxidant defence. Conclusions: The obtained results allow for improving the accuracy of cardiovascular risk prediction, and form personalised recommendations for the prevention and correction of its development. Full article

Transcranial direct current stimulation (tDCS) can modulate cortical excitability in a polarity-specific manner, yet identical protocols often produce inconsistent outcomes across sessions or individuals. This narrative review proposes that much of this variability arises from the brain’s intrinsic temporal landscape. Integrating evidence from chronobiology, sleep research, and non-invasive brain stimulation, we argue that tDCS produces reliable, polarity-specific after-effects only within a circadian–homeostatic “window of efficacy”. On the circadian (Process C) axis, intrinsic alertness, membrane depolarisation, and glutamatergic gain rise in the late biological morning and early evening, whereas pre-dawn phases are marked by reduced excitability and heightened inhibition. On the homeostatic (Process S) axis, consolidated sleep renormalises synaptic weights, widening the capacity for further potentiation, whereas prolonged wakefulness saturates plasticity and can even reverse the usual anodal/cathodal polarity rules. Human stimulation studies mirror this two-process fingerprint: sleep deprivation abolishes anodal long-term-potentiation-like effects and converts cathodal inhibition into facilitation, while stimulating at each participant’s chronotype-aligned (phase-aligned) peak time amplifies and prolongs after-effects even under equal sleep pressure. From these observations we derive practical recommendations: (i) schedule excitatory tDCS after restorative sleep and near the individual wake-maintenance zone; (ii) avoid sessions at high sleep pressure or circadian troughs; (iii) log melatonin phase, chronotype, recent sleep and, where feasible, core temperature; and (iv) consider mild pre-heating or time-restricted feeding as physiological primers. By viewing Borbély’s two-process model and allied metabolic clocks as adjustable knobs for plasticity engineering, this review provides a conceptual scaffold for personalised, time-sensitive tDCS protocols that could improve reproducibility in research and therapeutic gain in the clinic. Full article

Indigenous students remain significantly underrepresented in medical education, contributing to persistent health inequities in their communities. Systemic barriers, including cultural isolation, inadequate resources, and biased curricula, hinder their success. But what if generative artificial intelligence (GAI) could be the game-changer? This scoping review explores the potential of generative artificial intelligence (GAI) in making medical education more inclusive and supportive for Indigenous students through a comprehensive analysis of existing literature. From AI-powered engagement platforms to personalised learning systems and immersive simulations, GAI can be harnessed to bridge the gap. While GAI holds promise, challenges like biased datasets and limited access to technology must be addressed. To unlock GAI’s potential, we recommend faculty development, expansion of digital infrastructure, and Indigenous-led AI design. By carefully harnessing GAI, medical schools can take a crucial step towards creating a more diverse and equitable healthcare workforce, ultimately improving health outcomes for Indigenous communities. Full article

As survival rates for teenagers and young adults (TYAs) with cancer exceed 80%, they are living longer post treatment, yet often experience prolonged health and quality of life concerns. Many TYAs also experience unmet support needs. This study aimed to identify TYAs support needs following treatment at a UK hospital and explore how and when TYAs prefer to receive support. This study involved two phases: Phase 1 involved semi-structured interviews with 16 TYAs, 1–6 years post-treatment, aged 16–25 years at time of treatment completion and examined their experiences of support services, and preferences for future care. Phase 2 consisted of co-design workshops with eight TYAs and feedback from five healthcare/allied professionals (HCAPs) to refine and develop recommendations. Phase 1 findings revealed six key themes: (1) survivorship as disrupted continuity; (2) negotiating legitimacy and relational safety in help seeking; (3) support offered vs. support sought: pathways of referral and self-initiation; (4) emotional readiness as context dependent and non-linear; (5) support as an ecosystem, not a moment; and (6) personalised autonomy in support engagement. Phase 2 findings informed recommendations that emphasise the importance of flexible, personalised, and accessible post-treatment support, with pathways of care/support that can adapt to TYAs changing needs and preferences over time. Full article

Climate change is one of our most critical challenges, requiring urgent and comprehensive action across all levels of society. Individual actions and their roles in mitigating and adapting to climate change remain underexplored, despite global efforts. Under this context, this study was conducted to evaluate the ecological footprint of individuals for climate change mitigation. A structured online survey was designed and distributed through email lists, social media platforms, and community organisations to over 200 potential participants in the northwest of the UK. Due to the anonymous nature of the survey, only 83 individuals from diverse demographics completed the questionnaire. A carbon footprint calculator using conversion factors has been employed, based on energy consumption, travel, and material goods use. Participants are categorised into four groups based on their annual CO₂ emissions, ranging from less than 2 tonnes to over 10 tonnes. Personalised recommendations provided by the calculator focus on practical strategies, including adopting renewable energy, minimising unnecessary consumption, and opting for sustainable transportation. Results showed that only 5.5% of participants who employed advanced technologies and smart home technologies, 1.8% were implementing water-saving practices and 65.4% preferred to use their own car over other modes of transportation. In addition, the study found that 67.3% of participants had no or only a very limited knowledge of renewable energy technologies, indicating a need for education and awareness campaigns. The findings also highlight the importance of addressing demographic differences in ecological footprints, as these variations can provide insights into tailored policy interventions. Overall, despite the study’s limited sample size, this research contributes to the growing body of evidence on the importance of individual action in combating climate change and provides actionable insights for policymakers and educators aiming to foster a more sustainable lifestyle. Future studies with larger samples are recommended to validate and expand upon these findings. Full article

Although growing evidence suggests that digital personalised learning (DPL) has the potential to enhance learning outcomes, there is little research about the effective implementation and integration of DPL into the classroom. The aim of this study is to investigate the pedagogical implications of integrating a DPL tool into Kenyan early-grade classrooms to bridge the gap between theory and practice. This paper reports on systematic, design-based research conducted over three years, featuring five phases, each testing iterations to specific aspects of DPL implementation. The findings demonstrate that the pedagogic dimensions of classroom-integrated DPL are pivotal to its effective uptake and implementation. A key research contribution is the identification of a distinct gap between theoretical and practical conceptualisations of DPL, with teachers focused primarily on curriculum alignment and classroom management. The analysis also identified teachers’ central role in the process of personalisation, nuancing existing DPL frameworks by exploring interactions between the digital and classroom environments, as well as highlighting important considerations around access and equality. Recommendations include the co-design of DPL with teachers, drawing on their pedagogical perspectives to enhance integrative approaches. Full article

This study aimed to assess the clinical value of three-dimensional printed personalised vascular models (3DPPVMs) in assisting with the pre-operative planning and simulation of endovascular interventions. CT angiographic images of four cases, namely, abdominal aorta aneurysm (AAA), carotid artery stenosis, coronary artery stenosis, and renal artery stenosis, were selected, and 3DPPVMs were obtained. A total of 21 clinicians specialising in interventional radiology and vascular surgery were invited to participate in the study, comprising 6 radiologists and 15 vascular surgeons. Of these, 66.7% had not used a 3DPPVM prior to their participation. Considering all areas of experience and all four models, it was observed that 75% of the participants gave a ranking of 7 or above out of 10 with regard to the recommendation of the use of the 3DPPVMs. The mean scores of the participants’ ranking of the models ranged from 3.2 to 4.3 out of 5. The AAA model was ranked the highest for realism (4.10 ± 0.89, p = 0.002), the planning of interventions and simulations (3.90 ± 1.12 and 4.05 ± 0.95), the development of haptic skills (3.56 ± 0.98), reducing the procedure time (3.47 ± 1.12), and clarifying the pathology to patients (4.33 ± 0.69, p all >0.05), indicating consistency amongst the participants. The carotid artery model was ranked the highest for accurately displaying anatomical structures (4.3 ± 0.73). All the 3DPPVMs enhanced the understanding of the disease demonstrated, with rankings between 3.8 and 3.95. All the models aided in elucidating the intervention procedure required and in the planning of vascular interventions, with rankings of 3.5 and 3.9. The highest rankings were given by qualified clinicians with 8 or more years of experience. This study shows the potential value of using 3D-printed vascular models in education for clinicians and patients, as well as for clinical training and the pre-surgical simulation of endovascular stent-grafting procedures. Full article

The present study proposes adaptive augmented reality (AR) architecture, specifically designed to enhance real-time operator assistance and occupational safety in industrial environments, which is representative of Industry 4.0. The proposed system addresses key challenges in AR adoption, such as the need for dynamic personalisation of instructions based on operator profiles and the mitigation of technical and cognitive barriers. Architecture integrates theoretical modelling, modular design, and real-time adaptability to match instruction complexity with user expertise and environmental conditions. A working prototype was implemented using Microsoft HoloLens 2, Unity 3D, and Vuforia and validated in a controlled industrial scenario involving predictive maintenance and assembly tasks. The experimental results demonstrated statistically significant enhancements in task completion time, error rates, perceived cognitive load, operational efficiency, and safety indicators in comparison with conventional methods. The findings underscore the system’s capacity to enhance both performance and consistency while concomitantly bolstering risk mitigation in intricate operational settings. This study proposes a scalable and modular AR framework with built-in safety and adaptability mechanisms, demonstrating practical benefits for human–machine interaction in Industry 4.0. The present study is subject to certain limitations, including validation in a simulated environment, which limits the direct extrapolation of the results to real industrial scenarios; further evaluation in various operational contexts is required to verify the overall scalability and applicability of the proposed system. It is recommended that future research studies explore the long-term ergonomics, scalability, and integration of emerging technologies in decision support within adaptive AR systems. Full article

This paper presents strategies for effectively integrating AI tools into programming education and provides recommendations for enhancing student learning outcomes through intelligent educational systems. Learning computer programming is a cognitively demanding task that requires dedication, logical reasoning, and persistence. Many beginners struggle with debugging and often lack effective problem-solving strategies. To address these issues, this study investigates PyChatAI—a bilingual, AI-powered chatbot designed to support novice Python programmers by providing real-time feedback, answering coding-related questions, and fostering independent problem-solving skills. PyChatAI offers continuous, personalised assistance and is particularly beneficial for students who prefer remote or low-pressure learning environments. An empirical evaluation employing a Solomon Four-Group design revealed significant improvements across all programming skill areas, with especially strong gains in theoretical understanding, code writing, and debugging proficiency. Full article

The cold-start problem remains a significant challenge in recommendation systems, particularly in scenarios involving new users or items with insufficient historical interaction data, which severely limits the effectiveness of personalised recommendations. Despite extensive research efforts dedicated to addressing this issue, existing meta-learning approaches, while promising, often rely on the assumption that prior knowledge can be globally shared across all users. This assumption overlooks the inherent inefficiency of information sharing due to diverse user interests, frequently resulting in suboptimal solutions and constrained model performance. To address this limitation, we propose an enhanced meta-learning framework that leverages graph community detection algorithms to cluster users, enabling the extraction of unique prior knowledge within each cluster. This knowledge is then shared efficiently among users with similar interests within the same cluster. Through comparative experiments on cold-start recommendation tasks, our proposed model demonstrates superior performance over traditional methods, validating its effectiveness in improving cold-start recommendation accuracy. Furthermore, this study highlights potential application scenarios and future research directions for advancing cold-start solutions in recommendation systems. Full article

Gestational diabetes mellitus (GDM) is a complication that affects 20% of pregnancies worldwide. It is associated with adverse short- and long-term cardiometabolic outcomes for both mother and infant. Effective management of GDM involves lifestyle modifications, including medical nutrition therapy (MNT) and physical activity (PA), with the addition of insulin or metformin if glycaemic control remains inadequate. However, substantial gaps persist in the determination of optimal medical nutrition therapy (MNT) for women with GDM. Challenges in MNT include individual variation in glucose tolerance and changing maternal physiology and dietary requirements during pregnancy. Achieving optimal glycaemic control depends on careful macronutrient balance, particularly the distribution and quality of carbohydrate intake and sufficient protein and fat intake. Additionally, micronutrient deficiencies, such as inadequate vitamin D, calcium, and essential minerals, may exacerbate oxidative stress, inflammation, and glycaemic dysregulation, further impacting foetal growth and development. Cultural beliefs and dietary practices among pregnant women can also hinder adherence to recommended nutritional guidelines. Conditions like hyperemesis gravidarum (HG) affect ~1% to 2% of pregnant women can result in unintended energy and nutrient deficits. This special issue explores the current evidence and major barriers to optimising dietary therapy for women with GDM. It also identifies future research priorities to advance clinical practice, improve maternal and foetal outcomes, and address gaps in personalised nutrition interventions. Full article

The constant monitoring of the population’s diet and assessment of occupational exposure and environmental impacts are the key to determining health risks and understanding the factors contributing to potential abnormalities in developing lifestyle diseases. Extensive long-term lifestyle monitoring studies can provide data on population health risks, including the most common cardiovascular diseases like hypertension. This paper presents research recommendations for future researchers and doctors to improve the diagnosis of hypertension and targeted, personalised treatment. The research proposal includes a lifestyle study, a diagnostic panel with new biomarkers, and an environmental exposure assessment of men working in the metallurgical industry. New developments and improved interventions are constantly being sought, including new biomarkers with high diagnostic utility for cardiovascular diseases like hypertension. This should enable early diagnosis, and consequently allow for appropriate and, most importantly, personalised therapy, and prevent an increase in CVD deaths. Only the effective diagnosis, treatment, and monitoring of hypertension can reduce the risk of developing diseases associated with hypertension. I propose that several new parameters (NO, cfDNA, MPO, PCSK9, MyBPC3, microRNA, TAS, Pb, and Cd) with prognostic and/or predictive potential should be included in screening to confirm the need for the extensive testing of middle-aged men by healthcare professionals due to the risk of hypertension. Full article

Background/Objectives: The influence of individual differences in the selection of food portions can have a deep effect on recommendations for personalised nutrition. In addition to typical aspects such us energy density and nutrient composition, portion size is important for dietary recommendations. This study examined the dietary behaviours and portion size selection of 224 subjects in Spain and Germany to use such information to improve dietary adherence to a personalised nutrition app. Methods: An online questionnaire administered to adults in Spain and Germany collected sociodemographic data and dietary habits. The measurement of portion sizes was derived from a classification ranging from XXS to XL across 22 food groups, with assistance from a photographic atlas. Results: Significant differences across dimensions were found. Dietary habits showed that omnivores were the majority in both countries, with significant differences in the consumption of bread, desserts, and beverages. The Mediterranean diet was significantly followed by the Spanish group, reflecting cultural differences. Body mass index (BMI) was slightly higher among Germans, although both populations fell within the normal ranges. Portion size comparisons revealed statistically significant differences in the consumption of various food items between the two countries. Spaniards consumed higher amounts of rice, meat, and legumes, while Germans consumed larger portions of stews, lasagne, and pizza. These variations highlight differing dietary habits influenced by cultural preferences and dietary guidelines. Conclusions: The findings support the development of novel personalised nutrition apps that consider user preferences and enhance dietary adherence, thereby contributing to improved dietary recommendations and health outcomes. Full article

This narrative review provides an analysis of the role of nitric oxide (NO) and its precursors, particularly L-arginine, in vascular regulation and health, with an emphasis on findings from our experimental research in animal models. NO serves as a critical mediator of vascular function, contributing to vasodilation, the regulation of blood flow, and the prevention of thrombosis. As a primary precursor of NO, L-arginine is essential for maintaining endothelial integrity, modulating mitochondrial function, and reducing oxidative damage. This review synthesises the data and contextualises these findings within the physiological challenges faced by blood donors, such as repeated blood donation and associated oxidative stress. It examines the effects of L-arginine supplementation on mitochondrial respiration, lipid peroxidation, and microsomal oxidation in different conditions, including differences in age, gender, and dietary interventions. The mechanisms by which L-arginine enhances NO production, improves vascular elasticity, and alleviates endothelial dysfunction caused by reduced NO bioavailability are also investigated. By integrating experimental findings with insights from the existing literature, this review provides a perspective on the potential of L-arginine supplementation to address the specific physiological needs of blood donors. It highlights the importance of personalised nutritional approaches in enhancing donor recovery and vascular resilience. In addition, this review assesses the wider implications of L-arginine supplementation in mitigating oxidative stress and preserving vascular function. The interplay between NO bioavailability, dietary factors, and physiological adaptation in blood donors is highlighted, along with the identification of current knowledge gaps and recommendations for future research. By presenting both original experimental evidence and a critical synthesis of the literature, this article highlights the therapeutic potential of NO precursors, particularly L-arginine, in promoting vascular health in the context of blood donation. Full article