Search Results (827)

Background: Atrial fibrosis is a key contributor to atrial cardiomyopathy and can be assessed invasively using mean left atrial voltage (MLAV) from electroanatomical mapping. However, the invasive nature of this procedure limits its clinical applicability. Machine learning (ML), particularly regression tree-based models, may offer a non-invasive approach for predicting MLAV using clinical and echocardiographic data, improving non-invasive atrial fibrosis characterisation beyond current dichotomous classifications. Methods: We prospectively included and followed 113 patients with paroxysmal or persistent atrial fibrillation (AF) undergoing pulmonary vein isolation (PVI) with ultra-high-density voltage mapping (uHDvM), from whom MLAV was estimated. Standardised two-dimensional transthoracic echocardiography was performed before ablation, and clinical and echocardiographic variables were analysed. A regression tree model was constructed using the Classification and Regression Trees—CART-algorithm to identify key predictors of MLAV. Results: The regression tree model exhibited moderate predictive accuracy (R² = 0.63; 95% CI: 0.55–0.71; root mean squared error = 0.90; 95% CI: 0.82–0.98), with indexed minimum LA volume and passive emptying fraction emerging as the most influential variables. No significant differences in AF recurrence-free survival were found among MLAV tertiles or model-based generated groups (log-rank p = 0.319 and p = 0.126, respectively). Conclusions: We present a novel ML-based regression tree model for non-invasive prediction of MLAV, identifying minimum LA volume and passive emptying fraction as the most significant predictors. This model offers an accessible, non-invasive tool for refining atrial cardiomyopathy characterisation by reflecting the fibrotic substrate as a continuum, a crucial advancement over existing dichotomous approaches to guide tailored therapeutic strategies. Full article

With the advance of digital transformation, the assessment of the environmental impact of digital tools and technologies grows more relevant. Considering the inflated expectations of environmental responsibility in higher education, this study analyses how websites of Polish universities conform to sustainable web design criteria. The sustainability audit employed a methodology encompassing carbon emissions measurement, technical website analysis, and SEO evaluation. The author analysed 63 websites of public universities in Poland using seven independent audit tools, including an original AI Custom GPT agent preconfigured in the ChatGPT ecosystem. The results revealed a substantial differentiation in CO₂ emissions and website optimisation, with an average EcoImpact Score of 66.41/100. Nearly every fourth website exhibited a significant carbon footprint and excessive component sizes, which indicates poor asset optimisation and energy-intensive design techniques. The measurements exposed considerable variability in emission intensities and resource intensity among the university websites, suggesting the need for standardised digital sustainability practices. Regulations on the carbon footprint of public institutions’ websites and mobile applications could become vital strategic components for digital climate neutrality. Promoting green hosting, “Green SEO” practices, and sustainability audits could help mitigate the environmental impact of digital technologies and advance sustainable design standards for the public sector. The proposed auditing methodology can effectively support the institutional transition towards sustainable management of digital infrastructure by integrating technical, sustainability, and organisational aspects. Full article

The safety of lithium-ion batteries during transportation is critically important. However, current standards exhibit limitations, as their environmental testing parameter thresholds fail to fully encompass actual transportation conditions. To enhance both safety and standard applicability, in this study, we focused on four representative environmental conditions: temperature, vibration, shock, and low atmospheric pressure. Field measurements were conducted across road, rail, and air transport modes using a self-developed data acquisition system based on the NearLink communication technology. The measured data were then compared with the threshold values defined in current international and national standards. The results reveal that certain measured values exceeded the upper limits prescribed by existing standards, indicating limitations in their applicability under extreme transport conditions. Based on these findings, we propose revised testing parameters that better reflect actual transport risks, including a temperature cycling range of 72 ± 2 °C (high) and −40 ± 2 °C (low), a shock acceleration limit of 50 gn, adjusted peak frequencies in the vibration PSD profile, and a minimum pressure threshold of 11.6 kPa. These results provide a scientific basis for optimising safety standards and improving the safety of lithium-ion battery transportation. Full article

Background: Pain is a problem faced by critically ill surgical patients and has a major impact on their outcomes. Pain assessment is therefore essential for effective pain management, with a combination of pharmacological and non-pharmacological treatment. Clinical supervision, supported by models such as SafeCare, can improve professional development, safety and the quality of care in intensive care units. Objectives: This study aimed to: (1) assess current pain assessment practices in a polyvalent Intensive Care Unit (ICU) in the Porto district; (2) identify nurses’ training needs regarding the Clinical Supervision-Sensitive Indicator—Pain; and (3) evaluate the impact of clinical supervision sessions on pain assessment practices. Methods: A quantitative, quasi-experimental, cross-sectional study with a pre- and post-intervention design was conducted. Based on the SafeCare model, it included a situational diagnosis, 6 clinical supervision sessions (February 2023), and outcome evaluation via nursing record audits (November 2022 and May 2023) in 31 total critical ill patients. Pain was assessed using standardised tools, in line with institutional protocols. Data was analysed using Software Statistical Package for the Social Sciences v25.0. Results: Pain was highly prevalent in the first 24 h, decreasing during hospitalisation. Generalised acute abdominal pain predominated, with mild to moderate intensity, and was exacerbated by wound care and mobilisation/positioning. Pain management combined pharmacological and non-pharmacological treatment. There was an improvement in all the parameters of the pain indicator post-intervention. Conclusions: Despite routine assessments, gaps remained in reassessing pain post-analgesia and during invasive procedures. Targeted clinical supervision and ongoing training proved effective in improving compliance with protocols and supporting safer, more consistent pain management. Full article

Landscape quality and the productivity of $\mathit{Rhododendron\; simsii}$ are directly related to the maintenance of ecological functions in the alpine region. The specific relationship between the spatial pattern of colour patches and the visual quality of R. simsii landscape recreational forests has been insufficiently explored. In this study, we constructed a model of the relationship between landscape colour patches and the aesthetic value of such a forest, analysing the key factors driving changes in its landscape quality. A total of 1549 participants were asked to assess 16 groups of landscape photographs. The results showed that variations in perceived aesthetic quality were stimulated by colour patch dynamics and spatial heterogeneity. Utilising structural equation modelling (SEM), we identified key indicators synergistically influencing aesthetic quality, including the area percentage, shape, and distribution of colour patches, which demonstrated strong explanatory power ($R^2$ = 0.83). The SEM also revealed that the red patch area, mean perimeter area ratio, and separation index are critical latent variables with standardised coefficients of 0.54, 0.65, and 0.62, respectively. These findings provide actionable design strategies: (1) optimising chromatic contrast through high-saturation patches, (2) controlling geometric complexity, and (3) improving spatial coherence. These results advance the theoretical framework for landscape aesthetic evaluation and offer practical guidance for landscape recreational forest management. Full article

Background: Pancreatic cystic lesions (PCLs), including intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), pose a diagnostic challenge due to their variable malignant potential. Current guidelines, such as Fukuoka and American Gastroenterological Association (AGA), have moderate predictive accuracy and may lead to overtreatment or missed malignancies. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers the potential to improve risk stratification, diagnosis, and management of PCLs by integrating clinical, radiological, and molecular data. This is the first systematic review to evaluate the application, performance, and clinical utility of AI models in the diagnosis, classification, prognosis, and management of pancreatic cysts. Methods: A systematic review was conducted in accordance with PRISMA guidelines and registered on PROSPERO (CRD420251008593). Databases searched included PubMed, EMBASE, Scopus, and Cochrane Library up to March 2025. The inclusion criteria encompassed original studies employing AI, ML, or DL in human subjects with pancreatic cysts, evaluating diagnostic, classification, or prognostic outcomes. Data were extracted on the study design, imaging modality, model type, sample size, performance metrics (accuracy, sensitivity, specificity, and area under the curve (AUC)), and validation methods. Study quality and bias were assessed using the PROBAST and adherence to TRIPOD reporting guidelines. Results: From 847 records, 31 studies met the inclusion criteria. Most were retrospective observational (n = 27, 87%) and focused on preoperative diagnostic applications (n = 30, 97%), with only one addressing prognosis. Imaging modalities included Computed Tomography (CT) (48%), endoscopic ultrasound (EUS) (26%), and Magnetic Resonance Imaging (MRI) (9.7%). Neural networks, particularly convolutional neural networks (CNNs), were the most common AI models (n = 16), followed by logistic regression (n = 4) and support vector machines (n = 3). The median reported AUC across studies was 0.912, with 55% of models achieving AUC ≥ 0.80. The models outperformed clinicians or existing guidelines in 11 studies. IPMN stratification and subtype classification were common focuses, with CNN-based EUS models achieving accuracies of up to 99.6%. Only 10 studies (32%) performed external validation. The risk of bias was high in 93.5% of studies, and TRIPOD adherence averaged 48%. Conclusions: AI demonstrates strong potential in improving the diagnosis and risk stratification of pancreatic cysts, with several models outperforming current clinical guidelines and human readers. However, widespread clinical adoption is hindered by high risk of bias, lack of external validation, and limited interpretability of complex models. Future work should prioritise multicentre prospective studies, standardised model reporting, and development of interpretable, externally validated tools to support clinical integration. Full article

Aims/Background: Polypharmacy, or the concurrent intake of five or more medications, is a significant issue in clinical practice, particularly in multimorbid elderly individuals. Despite its importance for patient safety, medical education often lacks systematic training in recognising and managing polypharmacy within the framework of patient-centred care. We investigated the impact of a structured learning intervention introducing polypharmacy as a chronic condition, assessing whether it enhances medical students’ diagnostic competence, confidence, and interprofessional collaboration. Methods: A prospective cohort study was conducted with 50 final-year medical students who received a three-phase educational intervention. Phase 1 was interactive workshops on the principles of polypharmacy, its dangers, and diagnostic tools. Phase 2 involved simulated patient consultations and medication review exercises with pharmacists. Phase 3 involved reflection through debriefing sessions, reflective diaries, and standardised patient feedback. Student knowledge, confidence, and attitudes towards polypharmacy management were assessed using pre- and post-intervention questionnaires. Quantitative data were analysed through paired t-tests, and qualitative data were analysed thematically from reflective diaries. Results: Students demonstrated considerable improvement after the intervention in identifying symptoms of polypharmacy, suggesting deprescribing strategies, and working in multidisciplinary teams. Confidence in prioritising polypharmacy as a primary diagnostic problem increased from 32% to 86% (p < 0.01), and knowledge of diagnostic tools increased from 3.1 ± 0.6 to 4.7 ± 0.3 (p < 0.01). Standardised patients felt communication and patient-centredness had improved, with satisfaction scores increasing from 3.5 ± 0.8 to 4.8 ± 0.4 (p < 0.01). Reflective diaries indicated a shift towards more holistic thinking regarding medication burden. The small sample size limits the generalisability of the results. Conclusions: Teaching polypharmacy as a chronic condition in medical school enhances diagnostic competence, interprofessional teamwork, and patient safety. Education is a structured way of integrating the management of polypharmacy into routine clinical practice. This model provides valuable insights for designing medical curricula. Future research must assess the impact of such training on patient outcomes and clinical decision-making in the long term. Full article

Background: Chemotherapy-induced peripheral neuropathy is a common and debilitating side effect of cancer treatment. While exercise has shown promise in alleviating this burden, it remains underutilised in clinical practice due to the lack of accessible, clinician-friendly guidance. Aim: This review aimed to synthesise current evidence on exercise interventions for managing chemotherapy-induced peripheral neuropathy and provide practical insights to support clinicians in integrating these approaches into patient care. Methods: A search was conducted across MEDLINE, CINAHL, and SPORTDiscus using keywords related to exercise and CIPN. Studies were included if they involved adults receiving neurotoxic chemotherapy and exercise-based interventions. Two authors independently screened studies and resolved conflicts with a third author. Study quality was assessed using the JBI Critical Appraisal Tools, and only studies meeting a minimum quality standard were included. A balanced sampling approach was employed. Data on study design, participant characteristics, interventions, and outcomes were extracted. Results: Eleven studies were included, covering various exercise modalities: multimodal (n = 5), yoga (n = 2), aerobic (n = 1), resistance (n = 1), balance (n = 1), and sensorimotor (n = 1). Exercise interventions, particularly multimodal exercise, significantly improved symptom severity, functionality, and quality of life (p < 0.05). The studies had high methodological quality, with randomised controlled trials scoring between 9/13 and 11/13, and quasi-experimental studies scoring 8/9 on JBI tools. Conclusions: This review highlights the significant benefits of exercise, especially multimodal exercise, for managing CIPN and provides guidance for integrating these strategies into clinical practice. Future research is needed to refine exercise prescriptions and develop standardised guidelines. Full article

Background/Objectives: Borderline Personality Disorder (BPD) involves emotional dysregulation, interpersonal instability and impulsivity. Although treatments have advanced, evaluating the latest innovations remains essential. This rapid review aimed to (1) identify and classify recent therapeutic innovations for BPD, (2) assess their effects on clinical and functional outcomes, and (3) highlight research gaps to inform future priorities. Methods: Employing a rapid review design, we searched PubMed/MEDLINE, PsycINFO, and Embase for publications from 1 January 2019 to 28 March 2025. Eligible studies addressed adult or adolescent BPD populations and novel interventions—psychotherapies, pharmacological agents, digital tools, and neuromodulation. Two independent reviewers conducted screening, full-text review, and data extraction using a standardised form. Results: Sixty-nine studies—predominantly from Europe and North America—were included. Psychotherapeutic programmes dominated, ranging from entirely novel models to adaptations of established treatments (for example, extended or modified Dialectical Behavior Therapy). Pharmacological research offered fresh insights, particularly into ketamine, while holistic approaches such as adventure therapy and digital interventions also emerged. Most investigations centred on symptom reduction; far fewer examined psychosocial functioning, mortality, or social inclusion. Conclusions: Recent innovations show promise in BPD treatment but underserve the needs of mortality and societal-level outcomes. Future research should adopt inclusive, equity-focused agendas that align with patient-centred and recovery-oriented goals, supported by a coordinated, integrated research strategy. Full article

Compassion Focused therapy (CFT) is designed to reduce shame (internal and external) and self-criticism while enhancing the three flows of compassion (compassion to others, from others, and for the self). This systematic review evaluated the effectiveness of CFT on these core theoretical constructs in adult clinical populations. A systematic search of three databases (2000–2024) identified 21 studies (N = 450) meeting the inclusion criteria. The studies were narratively synthesised, and quality was assessed using the EPHPP tool. Consistent improvements in self-compassion (g = 0.23–4.14) and reductions in self-criticism (g = 0.29–1.56) were reported. Reductions in external shame were also observed (g = 0.54–1.22), though this outcome was examined in fewer studies. Limited and inconsistent evidence was found for internal shame and interpersonal compassion flows (compassion to and from others), with only a small number of low- to moderate-quality studies addressing these outcomes. Follow-up effects were rarely assessed, and comparator groups were limited. Most interventions were group-based and of variable methodological quality, with frequent selection bias, small sample sizes, and limited demographic diversity. Overall, CFT shows promise for targeting self-directed processes in clinical populations, though stronger evidence is needed to understand its effects on relational components of compassion. Future research should adopt standardised measures, improve methodological rigour, and recruit more diverse samples. Full article

Consumers are increasingly exposed to environmental claims on food products. These claims often lack scientific validation and there are different methodologies that can be used for grounding these claims, which can lead to misleading results. The European Union’s (EU) Environmental Footprint methodology excludes the aggregation of environmental impacts, including damage to human health. This fact reduces transparency and limits the consumers’ ability to make information-based sustainable choices. This study aims to address this issue by calculating aggregated impacts on human health via life cycle assessment (LCA) in the agriculture and food-production sectors. In the study the IMPACT World+ method was used, including trustworthy databases and proper functional unit definition. The assessment encompassed three types of vegetables, four types of fruit, and four types of ready meals. The study also attempts to assess the impact of different farming systems (organic and conventional) on human health. Two standardised functional units, i.e., the unit based on product weight and product energy value were considered for each group of products. Our findings showed significant differences in results when different functional units were used. Additionally, no conclusion could be drawn regarding which farming system is more sustainable. Therefore, it is essential that the regulator clearly defines the criteria for selecting the appropriate functional unit in LCA within the agriculture and food-production sectors. In the absence of these criteria, results should be presented for all alternatives. Although not required by EU regulation, the authors suggest that companies should nevertheless disclose information regarding the environmental impact of agriculture and food production on human health, as this is important for consumers. Full article

Alternative proteins denote non-traditional, high-protein foods. These innovative sources aim to compete with conventional animal products by providing protein-rich, sustainable, nutritious, and flavorful options. Currently, five main categories of alternative proteins are being developed: plant-based proteins, cultured meat, single-cell proteins, edible insects, and seaweed. Nonetheless, several chemical and microbiological food safety hazards are associated with these alternatives Incorporating novel protein sources into food products may heighten the prevalence of existing food allergies. This could arise from extracting proteins from their natural matrices and utilizing them at significantly higher concentrations. Additionally, the introduction of new proteins may lead to the development of novel food allergies. Proteins that are currently seldom or never consumed may cause primary sensitisation or trigger cross-reactivity with known allergens. To date, alternative proteins have not been thoroughly studied for their allergenic potential, and there is no standardised method for assessing this risk. This review aims to explore non-traditional protein sources, discussing their nutritional and functional properties, as well as their potential allergenicity based on available research. We conducted a literature search in PubMed and Embase databases. We used specific keywords and MESH terms. A total of 157 studies were included in the review. The studies reviewed in our analysis reveal significant limitations, such as inconsistent methodologies, limited participant numbers, and a lack of long-term data, which hinder the ability to make clear conclusions regarding the safety of these new proteins for individuals with allergies. To address current challenge, future research should integrate food science, regulatory perspectives and advanced technologies. Full article

Manual assembly remains essential in modern manufacturing, yet the increasing complexity of customised production imposes significant cognitive burdens and error rates on workers. Existing Spatial Augmented Reality (SAR) systems often operate passively, lacking adaptive interaction, real-time feedback and a control system with gesture. In response, we present Gest-SAR, a SAR framework that integrates a custom MediaPipe-based gesture classification model to deliver adaptive light-guided pick-to-place assembly instructions and real-time error feedback within a closed-loop interaction instance. In a within-subject study, ten participants completed standardised Duplo-based assembly tasks using Gest-SAR, paper-based manuals, and tablet-based instructions; performance was evaluated via assembly cycle time, selection and placement error rates, cognitive workload assessed by NASA-TLX, and usability test by post-experimental questionnaires. Quantitative results demonstrate that Gest-SAR significantly reduces cycle times with an average of 3.95 min compared to Paper (Mean = 7.89 min, p < 0.01) and Tablet (Mean = 6.99 min, p < 0.01). It also achieved 7 times less average error rates while lowering perceived cognitive workload (p < 0.05 for mental demand) compared to conventional modalities. In total, 90% of the users agreed to prefer SAR over paper and tablet modalities. These outcomes indicate that natural hand-gesture interaction coupled with real-time visual feedback enhances both the efficiency and accuracy of manual assembly. By embedding AI-driven gesture recognition and AR projection into a human-centric assistance system, Gest-SAR advances the collaborative interplay between humans and machines, aligning with Industry 5.0 objectives of resilient, sustainable, and intelligent manufacturing. Full article

The shift from traditional linear economic models to circular economy principles has gained momentum, particularly in the wastewater treatment sector. This transition relies on resource use efficiency, waste minimisation, and value creation. Nevertheless, a review of 83 articles, yielding 386 key performance indicators, reveals a significant lack of standardised methodologies for assessing circular economy performance in Wastewater Treatment Plants. This absence allows ambiguous outcomes and hinders comparative analyses across facilities and countries. There is a clear need for a universally applicable Key Performance Indicators-based framework that evaluates all environmental dimensions without forgetting the technical component while encompassing the various circularity strategies. Such a framework should not discard defined parameters’ costs and data availability, avoiding abstract parameters that may lead to data manipulation and require extensive additional costs. Standardising Key Performance Indicators with explicit units of measurement is essential to ensure data comparability. In addition, they should comply with the regulatory institutions’ evaluation requirements, at least while these methodologies have not yet evolved to incorporate broader circular and environmental considerations. Developing a standardised Key Performance Indicators-based framework is crucial for effectively evaluating circular economy performance in Wastewater Treatment Plants, ensuring comprehensive environmental assessment, data comparability, and alignment with regulatory standards. This approach would facilitate a more consistent and transparent evaluation of circular economy initiatives across diverse contexts. Full article

Sustainability in the non-alcoholic beverage industry requires effective metrics to assess environmental, social, and economic performance. However, the lack of standardised indicators for small and medium-sized enterprises (SMEs) hinders the implementation of sustainable strategies. This study aims to select a set of sustainability indicators for small and medium-sized non-alcoholic beverage industries in Brazil. Seventy-four indicators were identified based on the Global Reporting Initiative (GRI) guidelines, which were subsequently evaluated and refined by industry experts for prioritisation. Statistical analysis led to the selection of 31 final indicators, distributed across environmental (10), social (12), and economic (9) dimensions. In the environmental dimension, priority indicators include water management, energy efficiency, carbon emissions, and waste recycling. The social dimension highlights working conditions, occupational safety, gender equity, and impacts on local communities. In the economic dimension, key indicators relate to supply chain efficiency, technological innovation, financial transparency, and anti-corruption practices. The results provide a robust framework to guide managers in adopting sustainable practices and support policymakers in improving the environmental, social, and economic performance of small and medium-sized non-alcoholic beverage industries. Full article