Search Results (49)

We aimed to determine the influence of donors’ vascular function on renal function in recipients and to evaluate the role of Orai1 calcium channels as a potential marker. A prospective collaborative multicenter study was designed. Blood, aorta (HA), mesenteric arteries (HMAs) and corpus cavernosum (HCC) specimens were obtained from organ donors at the kidney procurement procedure (n = 60). Evolution (up to 2 years) of renal function measured as serum creatinine (SCr) and glomerular filtration rate (GFR) was evaluated in respective recipients (n = 64). Vascular responses were determined in HA, HMA and HCC from donors. Tumor necrosis factor-α, asymmetric dimethyl arginine and Orai1 were measured in plasma. Orai1 protein expression was also evaluated in each donor’s aorta. Endothelium-dependent vasodilation (HMA, HCC) and adrenergic contraction (HA) in donors determined renal function in recipients, 12 months post-transplantation. Donors in the best quartile of vascular function predicted lower SCr and higher GFR in kidney recipients for 12/24 months. Plasma Orai1 in donors was negatively correlated with vascular function and predicted renal function at 3–6 months post-transplantation. Donor Orai1 vascular content was associated with reduced vascular function and with poorer recipient renal function for 1-year post-transplantation. Systemic vascular function of kidney donors determines recipients’ renal function at short/mid-term. Donors’ vascular function and recipients’ renal function are negatively associated with donors’ Orai1 vascular expression, being a potential biomarker of renal outcomes. Full article

In an era marked by climate change, socio-economic disparity, and digital acceleration, the challenge of effectively communicating complex environmental data to diverse audiences has become increasingly urgent. This study examines how data visualization and interactive art can be employed to translate scientific knowledge into engaging, accessible formats that raise public awareness and encourage climate action. We reviewed 495 environmental art and design projects and selected a corpus of 36 that focus on dynamic data visualisation and interactive experience design in response to climate and urban challenges. A multi-scalar, qualitative analysis was conducted to assess the thematic structure, technological strategies, and aesthetic dimensions of these projects. Our findings show that interdisciplinary methods—blending real-time data, machine learning, sonification, and immersive installations—are effective in rendering abstract climate data into emotionally resonant, actionable narratives. Through detailed case studies, we demonstrate how artistic interventions can facilitate public understanding, stimulate behavioural change, and support participatory urban adaptation. We conclude by highlighting the significance of collaborative, cross-sectoral approaches in designing future-oriented communication strategies for climate resilience, and by identifying future research pathways in the integration of environmental science, technology, and the arts. Full article

This study provides a bibliometric analysis of polarity sensitivity research from 1980 to 2023, examining intellectual structure, collaboration patterns, and emerging trends. Analysing 835 documents using Bibliometrix (V.4.1.0), CiteSpace (V.6.1.R6), and VOSviewer (V1.6.18), we identify three evolutionary phases: (1) foundational theoretical development (1980–2000), transitioning from syntactic to semantic-based theories; (2) methodological diversification (2000–2010), incorporating cognitive–pragmatic frameworks and corpus-based studies; and (3) contemporary integration (2010–2023), marked by multidisciplinary approaches. Co-citation analysis reveals three intellectual clusters centred on formal semantics, pragmatic approaches, and minimalist frameworks. Geographic analysis shows the United States as the leading contributor, followed by Germany and the United Kingdom. Collaboration network analysis underscores intensive transatlantic exchanges and emerging computational contributions from Asia. Keyword co-occurrence analysis (165 terms) demonstrates theoretical sophistication and empirical integration, with growing interest in neurocognitive approaches, cross-linguistic variations, and interface phenomena. Challenges include reconciling universal principles with language-specific variations and integrating processing models with formal theories. Promising research directions involve the combination of computational modelling, diachronic studies, and applications in language teaching and natural language processing. This study maps the intellectual landscape of polarity sensitivity research while suggesting future directions toward unified theories that address universal and language-specific patterns. Full article

In an era of evolving scholarly ecosystems, machine learning (ML) and artificial intelligence (AI) have become pivotal in advancing research impact analysis. Despite their transformative potential, the fragmented body of literature in this domain necessitates consolidation to provide a comprehensive understanding of their applications in multidimensional impact assessment. This study bridges this gap by employing bibliometric methodologies, including co-authorship analysis, citation burst detection, and advanced topic modelling using BERTopic, to analyse a curated corpus of 1608 scholarly articles. Guided by three core research questions, this study investigates how ML and AI enhance research impact evaluation, identifies dominant methodologies, and outlines future research directions. The findings underscore the transformative potential of ML and AI to augment traditional bibliometric indicators by uncovering latent patterns in collaboration networks, institutional influence, and knowledge dissemination. In particular, the scalability and semantic depth of BERTopic in thematic extraction, combined with the visualisation capabilities of tools such as CiteSpace and VOSviewer, provide novel insights into the dynamic interplay of scholarly contributions across dimensions. Theoretically, this research extends the scientometric discourse by integrating advanced computational techniques and reconfiguring established paradigms for assessing research contributions. Practically, it provides actionable insights for researchers, institutions, and policymakers, enabling enhanced strategic decision-making and visibility of impactful research. By proposing a robust, data-driven framework, this study lays the groundwork for holistic and equitable research impact evaluation, addressing its academic, societal, and economic dimensions. Full article

Background: Injury to the anterior cruciate ligament is one of the most common knee injuries. Following anterior cruciate ligament reconstruction, strength deficits and reduced quadriceps and hamstring muscle mass are common. Traditional strengthening protocols recommend the use of heavy loads. However, following surgery, heavy-load exercises are contraindicated to protect the joint and graft. Blood flow restriction resistance training is an alternative that optimizes muscle recovery. The aim of this study was to evaluate the effects of blood flow restriction resistance training on muscle mass and strength after ACLR. Methods: The Pubmed, Cochrane Library, and PEDro databases were used to constitute the corpus of this systematic review. The methodological quality of the studies was assessed with the Cochrane Collaboration’s analysis grid. Results: Thirty-four articles were identified in the initial search, and five randomized controlled trials were included in this review. Not all studies reported significant results regarding strength and muscle mass. Two of these studies observed a significant improvement in strength associated with blood flow restriction resistance training compared with the control group. A significant increase in muscle mass was observed in one study. Conclusions: The blood flow restriction resistance training method shows superior efficacy to training without occlusion, yet this device has not been shown to be more effective than heavy-load resistance training in terms of muscular strength and muscle mass. Blood flow restriction resistance training shows superior efficacy in both these variables when used with low loads. However, there are still few random controlled trials on this subject, and this review presents their limitations and biases. Future research is needed on guidelines for the application of blood flow restriction resistance training in clinical populations. Full article

Although scientific literature already shows the connections between gamification, tourism and sustainability, there is still a gap in research into how gamification can transform cities into more playful, sustainable and intelligent tourism destinations. Gamification in tourism promotes engagement, education and innovation, enriching experiences and driving the development of smart destinations. This study advances this debate by employing a mixed methodology, combining a systematic review (supported by Rayyan, an artificial intelligence) and content analysis (supported by Iramuteq, a textual software, version 0.7 Alpha 2). Additionally, we used Dreamscape by Voyant Tools online for a geographical analysis of the textual corpus formed by n = 61 abstracts. The intersection of main findings from the techniques applied provides eight new insights on urban tourism education through gamification, which are as follows: (1) exchange between the Global North and South; (2) local and global participation; (3) collaborations to achieve the 17 SDGs; (4) improve urban destination marketing by ethics principles; (5) deepening disruption technology and ethics studies; (6) the transformation of reality by sciences; (7) gamification on interdisciplinary perspectives; and (8) the role of gamification on the tourism and hospitality industry. The results suggest that gamification in tourism can offer a new ontological approach to addressing sustainable urban development, highlighting both theoretical and practical challenges. This research has both theoretical and practical implications, advancing the concept of ecogaming by integrating technology, entertainment and sustainability, and reinforces its potential to educate and engage in sustainable urban tourism practices. Full article

Background/Objectives: Artificial intelligence (AI) is gaining an increasing amount of influence in various fields, including medicine. In radiology, where diagnoses are based on collaboration between diagnostic devices and the professional experience of radiologists, AI intervention seems much easier than in other fields, but this is often not the case. Many times, the patients orient themselves according to the doctor, which is not applicable in the case of AI. Another limitation rests in the controversies regarding medico-legal liability. In the field of radio-imaging in Romania, the implementation of AI systems in diagnosis is at its beginning. An important aspect of this is raising awareness among the population about these assistive AI systems and, also, awareness of the technological evolution of AI among medical staff. This narrative review manuscript analyzes the existing literature data regarding the medico-legal aspects of AI application in radiology, highlighting the controversial aspects and the lack of statutory legislative regulations in Romania. Methods: A detailed search was conducted across three electronic databases including MEDLINE/PubMed, Scopus, and Web of Science, with 53 papers serving as the literature corpus of our review. Results: General requirements for artificial intelligence systems used in radiology have been established. In the radiological diagnostic process, there are five levels of AI system implication. Until now, completely autonomous AI systems have not been used. Regarding liability in the case of malpractice, at the currently accepted legislative level, most of the time, the radiologist is liable for their own fault or non-compliant use of diagnostic AI systems. Controversies arise in the case of radio-imaging diagnosis in which AI systems act autonomously. Conclusions: In order for AI diagnostic radio-imaging systems to be implemented, they must meet certain quality standards and be approved. The radiologist must know these systems, accept them, know their limits, and validate them in accordance with their degree of involvement in radiological diagnosis. Considering the evolution of technology in the Romanian medical system, including radiology, in the future, an alignment with the legal standards established/proposed at the European level is desired. Full article

This study investigates how religious rhetoric and popular mobilisation contributed to the preservation and propagation of Daoist traditions at the mountain Maoshan 茅山 from late Qing to Republican China (1864–1937), focusing particularly on the corpus of religious texts related to Maoshan and its tutelary gods, the Three Mao Lords 三茅真君. Through a detailed analysis of primary sources, including editions of the Maoshan Gazetteer, liturgical manuals such as the scripture (jing 經), litany (chan 懺), and performative texts such as the precious scroll (baojuan 寶卷) of the Three Mao Lords, this study identifies six key rhetoric strategies employed by Maoshan Daoists, using the acronym IMPACT: (1) Incorporation: Appending miracle tales (lingyan ji 靈驗記) and divine medicine (xianfang 仙方) to address immediate and practical needs of contemporary society; (2) Memory: Preserving doctrinal continuity while invoking cultural nostalgia to reinforce connections to traditional values and heritage; (3) Performance: Collaborating with professional storytellers to disseminate vernacularized texts through oral performances, thereby reaching broader audiences including the illiterate. (4) Abridgment: Condensing lengthy texts into concise and accessible formats; (5) Canonization: Elevating the divine status of deities through spirit-writing, thereby enhancing their religious authority; (6) Translation: Rendering classical texts into vernacular language for broader accessibility. Building upon J.L. Austin’s speech act theory, this study reconceptualizes these textual innovations as a form of “text acts”, arguing that Maoshan texts did not merely transmit religious doctrine but actively shaped pilgrimages and devotional practices through their illocutionary and perlocutionary force. Additionally, this study also highlights the crucial role of social networks, particularly the efforts of key individuals such as Zhang Hefeng 張鶴峰 (fl. 1860–1864), Long Zehou 龍澤厚 (1860–1945), Jiang Daomin 江導岷 (1867–1939), Wang Yiting 王一亭 (1867–1938) and Teng Ruizhi 滕瑞芝 (fl. 1920–1947) who facilitated the reconstruction, reprinting and dissemination of these texts. Furthermore, this study considers pilgrimages to Maoshan as a form of popular mobilisation and resistance to anti-clerical and anti-superstition campaigns, illustrating how, against all odds, Maoshan emerged as a site where religious devotion and economic activity coalesced to sustain the local communities. Ultimately, despite the challenges identified in applying speech act theory to textual practices, the findings conclude that the survival and revival of Daoist traditions at Maoshan was not only a result of textual retention and innovation but also a testament to how religious rhetoric, when coupled with strategic social engagement, can fuel popular mobilisation, reignite collective devotion, and reshape cultural landscapes in transformative ways. Full article

The rapid expansion of medical information has resulted in named entity recognition (NER) and relation extraction (RE) essential for clinical decision support systems. Medical texts often contain specialized vocabulary, ambiguous abbreviations, synonyms, polysemous terms, and overlapping entities, which introduce significant challenges to the extraction process. Existing approaches, which typically rely on single models such as BiLSTM or BERT, often struggle with these complexities. Although large language models (LLMs) have shown promise in various NLP tasks, they still face limitations in handling token-level tasks critical for medical NER and RE. To address these challenges, we propose COMCARE, a collaborative ensemble framework for context-aware medical NER and RE that integrates multiple pre-trained language models through a collaborative decision strategy. For NER, we combined PubMedBERT and PubMed-T5, leveraging PubMedBERT’s contextual understanding and PubMed-T5’s generative capabilities to handle diverse forms of medical terminology, from standard domain-specific jargon to nonstandard representations, such as uncommon abbreviations and out-of-vocabulary (OOV) terms. For RE, we integrated general-domain BERT with biomedical-specific BERT and PubMed-T5, utilizing token-level information from the NER module to enhance the context-aware entity-based relation extraction. To effectively handle long-range dependencies and maintain consistent performance across diverse texts, we implemented a semantic chunking approach and combined the model outputs through a majority voting mechanism. We evaluated COMCARE on several biomedical datasets, including BioRED, ADE, RDD, and DIANN Corpus. For BioRED, COMCARE achieved F1 scores of 93.76% for NER and 68.73% for RE, outperforming BioBERT by 1.25% and 1.74%, respectively. On the RDD Corpus, COMCARE showed F1 scores of 77.86% for NER and 86.79% for RE while achieving 82.48% for NER on ADE and 99.36% for NER on DIANN. These results demonstrate the effectiveness of our approach in handling complex medical terminology and overlapping entities, highlighting its potential to improve clinical decision support systems. Full article

Recent research underscores the significance of data-led and collaborative reflection in enhancing teaching practices and professional development of teachers. While video-based reflections have been extensively studied, the potential of corpus-based methods remains underexplored. We address this gap in two ways. Firstly, we describe a research and development project in which we employed a corpus linguistic tool to analyse and reflect on our own lectures, with the ulterior aim of promoting a corpus-based reflective practice (CBRP) model that would help other teachers do the same. Secondly, we introduce collaborative micro auto-ethnography (CMAE) to illustrate how our model can facilitate the noticing of different aspects of teacher talk and lead to, what we call, snowball reflections (i.e., a sequence of reflective talk that extends a topic and spreads from one participant to the next). Our approach shows that corpus analysis, coupled with collaborative reflections, has the potential to not only enhance language use but also stimulate broader and deeper pedagogical discussions on and insights into teaching styles and student engagement, going beyond analyses of single words and lexico-grammatical patterns in teacher talk. We argue that there should be institutional support for developing new corpus-based professional development initiatives and that researchers can benefit from using ethnographic data together with detailed analyses of interactions. Full article

This research responds to the increasing call to hone Student Teachers’ (ST) skill of selective attention or professional noticing as an essential early step toward developing noticing in the classroom environment. Furthering the line of research on teacher noticing via videos, this study aims to identify the distinguishing features of STs’ noticing in a Corpus Linguistics approach and enhance our understanding of STs’ habitual ways of thinking, doing, and feeling in using videos to reflect collaboratively. Participants were 40 final-year STs majoring in English Language Education and five tutors from the same university. During their 8 weeks of professional practicum, STs recorded their classes and reflected on their practice using the Self-Evaluation of Teacher Talk through Video Enhanced Observation (SETTVEO) tag set and as part of online professional learning communities. A 200,000-word Corpus of Dialogic Reflection (CoDR) was constructed and analysed using the #LancsBox 6.0 tool. Findings highlight novice teachers’ unconsciousness or problematisation of their personal agency, haste in proposing alternative practices, and cognitive or emotional dissonance when they analyse their own videos in group settings. This study has implications for the guidance needed in technology-enhanced dialogic reflection. It suggests how teacher educators could tailor their support to the reflective and professional needs of novice teachers for a more productive, transformative reflection and teacher-learning experience. Full article

In this study, we provide a comparative analysis of intangible cultural heritage (ICH) research trends in Korea and Japan, aiming to uncover shared knowledge networks and potential areas for sustainable cooperation. We employ a mixed-method approach, combining machine learning-driven topic modeling using Latent Dirichlet Allocation (LDA) and network analysis techniques, to examine a corpus of Korean and Japanese research papers on ICH. LDA topic modeling identified three primary themes: technology and ICH, safeguarding ICH, and methodologies and approaches in ICH research. Comparative analysis reveals distinct characteristics in each country’s approach. Korean research emphasizes practical applications of technology and policy-driven safeguarding strategies, while Japanese research leans towards theoretical exploration and cross-cultural comparisons. Citation network analysis further identifies influential papers and shared knowledge bases, underlining potential opportunities for collaboration. Key findings highlight the potential of technology for ICH preservation and promotion, the necessity of comprehensive safeguarding strategies, and the crucial role of community engagement. Our study suggests that by leveraging their complementary strengths and engaging in collaborative research, Korea and Japan can contribute to the sustainable safeguarding of ICH and foster a deeper understanding of their shared cultural heritage. Full article

This study discusses financial sustainability in retirement via a comprehensive bibliometric and content analysis on financial literacy and retirement preparedness for 1970–October 2024. Data for this study are extracted from the Scopus database. In line with the PRISMA guideline, the inclusion and exclusion criteria were applied, and the final corpus of articles used in the analysis is 518 articles. These articles are analyzed using the bibliometric methods: Bibliometrix R-package (Biblioshiny) version 4.0 and VOSviewer 1.6.20. The descriptive analysis identified the publication trends, prominent authors, articles, sources, country collaboration, and quadrant analysis. Based on the bibliographic coupling, the following themes were identified: (1) financial literacy and retirement planning, (2) behavioral finance and psychological determinants of retirement planning and preparedness, (3) financial risk, decision making, and retirement preparedness, and (4) gender, socioeconomic factors, and financial literacy disparities. This study has consolidated and enriched the understanding of how financial literacy, education, psychology and behaviors, and financial risk play a pivotal and central role in shaping long-term financial sustainability, as well as retirement planning and preparedness. From a practical perspective, an all-inclusive understanding of the above is essential for guiding regulators and policymakers in designing policies, targeted interventions, and programs that empower individuals towards retirement preparedness, considering regional, socioeconomic, and gender-related factors. Full article

The forty-year-old Long-Term Ecological Research MareChiara (LTER-MC) program started on 26 January 1984, with fortnightly oceanographic sampling until 1991 and then, from 1995, with weekly sampling up to the present time. LTER-MC produced >150 publications that have been cited by thousands of other studies. In this scoping review, we analyzed this corpus using a semantic approach based on topic modeling, a machine-driven procedure to identify and map topics and their interactions. Understanding the causes behind the evolution of scientific topics, their emergence, splitting, hybridization, or merging within a scientific community is an important step in science policy in managing collaborative research and bringing it into the future. Across different topics, mainly represented by studies on Natural History, Biodiversity, Phenology, Life Cycles, and Community Ecology, the LTER-MC work expanded the knowledge on planktonic organisms, describing in detail their lifestyles and delineating their relationships with environmental conditions. In presenting these results, the potential strengths, weaknesses, opportunities, and threats connected to the overall scientific dimension of LTER-MC are discussed. Finally, the upcoming effort is envisioned in reinforcing internal collaboration to integrate basic and applied research around scientific investigations suitable for establishing a stronger interaction between science and policy, as indicated by the United Nations Decade of Ocean Science for Sustainable Development. Full article

The advent of the digital era has catalyzed transformative shifts in educational paradigms, seamlessly integrating sustainable education with innovative pedagogical approaches that cater to a broader spectrum of learners and educators. As the academic landscape evolves into an information-dense environment, traditional models of one-on-one feedback often fall short, resulting in delays and a lack of diverse intellectual engagement. This shift underscores the growing importance of peer feedback and asynchronous post-lesson feedback mechanisms, which have emerged as vital, process-oriented educational activities. Such mechanisms not only address the immediacy and diversity of feedback but also foster a sustainable, collaborative, and engaging learning environment that bolsters student autonomy and interaction. This study conducts a mapping review and synthesis of the literature on peer interaction, assessment, and participatory innovations in online and mobile learning within higher education, covering the period from 1993 to 2022. By employing topic modeling techniques to analyze a corpus of 485 articles, the literature was initially segmented into 19 distinct issues. These were subsequently consolidated into three major thematic models, thereby highlighting significant themes, research focal points, and prospective trends. This analytical process not only enriches the understanding of peer dynamics in digital learning settings but also integrates sustainable educational practices by promoting resource efficiency and inclusivity. The findings serve as a robust reference for future researchers aiming to explore the intersections of technology, peer interaction, and sustainability in educational settings. Full article