Collaborative Research Network Research Articles

Research Trends and Hot Spots in Telemedicine for the Elderly: A Scientometric Analysis.

As the elderly population rapidly grows, age-related health issues are increasing. Telemedicine helps older adults adapt by providing efficient and accessible health management and medical services. This study employs bibliometric analysis to examine research focus areas, emerging trends, and collaboration networks in telemedicine for older adults over the past three decades. The Web of Science Core Collection served as the primary data source for the publications on telemedicine and the elderly since the database's inception through June 2024. Using CiteSpace.6.2.R4 software, keyword and collaboration network visualizations were generated, including clusters, co-authors, and co-citations. This study analyzed 586 papers from 252 countries or regions, which were published across 246 journals and written by 2750 authors. The analysis revealed three primary research directions encompassing 42 clusters: (1) health literacy and technology adaptation; (2) telemedicine technology and health management; and (3) social interaction and economic impact. Research hotspots include elderly fitness, mobile health, technology acceptance, telemedicine, elderly care, and health literacy. Despite the potential benefit of telemedicine, challenges persist in areas such as technology acceptance, usability, effectiveness, service quality, and privacy concerns. This review provides a comprehensive overview of current research on telemedicine for the elderly and highlights emerging trends in the field.

The development of resilience research in critical infrastructure systems: A bibliometric perspective.

Critical infrastructure systems (CISs) are the cornerstone of modern cities. Substantial economic losses and social impacts are caused once natural disasters or man-made disruptions attack the CISs. As a "resilient city" has become an essential theme of communities' sustainable development, research on resilience and its practice in industries boost the CISs' capacity to respond and adapt to changing environments. From the Web of Science (WOS) Core Collection, this study screened 1,247 scientific articles related to resilience in CISs and conducted a bibliometric analysis to investigate the evolution and future potential in this field. Topic visualized networks were constructed for CIS resilience using CiteSpace, a dedicated tool for visualizing and analyzing trends and patterns in scientific literature. The results demonstrate collaborative research networks among countries, institutions, main scholar/group networks, and leading journals publishing CIS resilience work. This study also explained how the research interest evolved over the last 20 years and found the current frontiers pointing to "power systems resilience" and "supply chain resilience." The reasons were discussed subsequently from the perspectives of the influence that natural hazards (based on the EM-DAT data) and government policies have upon CISs' resilience work.

Exploring molecular mechanisms of exercise on metabolic syndrome: a bibliometric and visualization study using CiteSpace.

To investigate the molecular mechanisms through which exercise influences metabolic syndrome (MS) and identify key research trends and collaborative networks using bibliometric and visualization techniques. We conducted a systematic literature search using the Web of Science Core Collection for articles published from 2014 to 2023. Using CiteSpace, we performed a bibliometric analysis of 562 eligible papers, generating visual knowledge maps to identify prevailing patterns, popular subjects, and emerging trends in the literature. The study reveals that exercise mitigates MS by reversing high-fat diet-induced abdominal obesity, reducing lipid accumulation and inflammation, enhancing insulin sensitivity, and improving cardiovascular function. Key molecular pathways include PPAR-γ/CPT-1/MCAD signaling, AMPK activation, and nitric oxide production. The USA leads in research output, with significant contributions from American institutions. Collaboration among researchers is limited, highlighting the need for more extensive and high-quality research initiatives. Regular, moderate-to-high-intensity exercise is crucial for managing MS. Exercise activates beneficial molecular pathways, improving metabolic health and cardiovascular function. Future research should focus on expanding collaborations and exploring novel molecular targets to enhance the therapeutic potential of exercise in metabolic syndrome management.

Collaborative research networks in Higher Education An integrative review

This study investigates the determining factors for the success of collaborative research networks in higher education, focusing on the formation, development, and operation of these networks. Despite the growing recognition of the importance of collaborative networks, there is a lack of studies that comprehensively identify the critical factors for their success. Utilizing an integrative literature review, the research was conducted in the ERIC, Web of Science (WOS), and Scopus databases. The main research question is: what factors influence the success of collaborative research networks in higher education? The findings indicate that the effectiveness of these networks is influenced by structural, functional, and evaluative factors. The integration of information and communication technologies (ICT) is essential for effective collaboration, allowing geographically dispersed teams to work together efficiently. The study concludes that public policies that value collaborative networks are necessary to promote an environment conducive to integration and scientific collaboration. This study contributes to the understanding of the elements that influence the success of collaborative research networks, highlighting the importance of integration, diversity, funding, and knowledge sharing, with significant implications for the formulation of policies and practices in higher education.

Advances in Shear Stress Stimulation of Stem Cells: A Review of the Last Three Decades.

Stem cells are widely used in scientific research because of their ability to self-renew and differentiate into a variety of specialized cell types needed for body functions. However, the self-renewal and differentiation of stem cells are regulated by various stimuli, with mechanical stimulation being particularly notable due to its ability to mimic the physical environment in the body. This study systematically collected 2638 research papers published between 1994 and 2024, employing tools such as VOSviewer, CiteSpace, and GraphPad Prism to uncover research hotspots, publication trends, and collaboration networks. The results indicate a yearly increase in global research on the shear stress stimulation of stem cells, with significant contributions from the United States and China in terms of research investment and output. Future research directions include a deeper understanding of the mechanisms underlying mechanical stimulation's effects on stem cell differentiation, the development of new materials and scaffold designs to better replicate the natural cellular environment, and advancements in regenerative medicine. Despite considerable progress, challenges remain in translating basic research findings into clinical applications.

PSInet: a new global water potential network.

Given the pressing challenges posed by climate change, it is crucial to develop a deeper understanding of the impacts of escalating drought and heat stress on terrestrial ecosystems and the vital services they offer. Soil and plant water potential play a pivotal role in governing the dynamics of water within ecosystems and exert direct control over plant function and mortality risk during periods of ecological stress. However, existing observations of water potential suffer from significant limitations, including their sporadic and discontinuous nature, inconsistent representation of relevant spatio-temporal scales and numerous methodological challenges. These limitations hinder the comprehensive and synthetic research needed to enhance our conceptual understanding and predictive models of plant function and survival under limited moisture availability. In this article, we present PSInet (PSI-for the Greek letter Ψ used to denote water potential), a novel collaborative network of researchers and data, designed to bridge the current critical information gap in water potential data. The primary objectives of PSInet are as follows. (i) Establishing the first openly accessible global database for time series of plant and soil water potential measurements, while providing important linkages with other relevant observation networks. (ii) Fostering an inclusive and diverse collaborative environment for all scientists studying water potential in various stages of their careers. (iii) Standardizing methodologies, processing and interpretation of water potential data through the engagement of a global community of scientists, facilitated by the dissemination of standardized protocols, best practices and early career training opportunities. (iv) Facilitating the use of the PSInet database for synthesizing knowledge and addressing prominent gaps in our understanding of plants' physiological responses to various environmental stressors. The PSInet initiative is integral to meeting the fundamental research challenge of discerning which plant species will thrive and which will be vulnerable in a world undergoing rapid warming and increasing aridification.

Application of artificial intelligence in rheumatic disease: a bibliometric analysis

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. “Scientific Reports” emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.

The Multi-Country Multi-City (MCC) Collaborative Research Network: novel study designs and methodological advancements for multicentric studies

The Multi-Country Multi-City (MCC) Collaborative Research Network: novel study designs and methodological advancements for multicentric studies

Mapping Research Trends and Collaborative Networks in Swarm Intelligence for Healthcare Through Visualization.

Swarm intelligence, evolved from the self-organized behavior of social insects, has become an essential method under artificial intelligence for handling complex and dynamic issues. This study visualizes and analyzes the use of swarm intelligence in healthcare, focusing on its role in managing rising medical data complexity, optimizing diagnostic and therapeutic solutions, and supporting personalized healthcare. The analysis, based on literature from Scopus (2003-2024) using Biblioshiny and VOSviewer, reveals a strong increase in publications since 2017, with central themes around disease diagnosis, treatment optimization, medical image analysis, and real-time patient monitoring through frameworks like the Internet of Medical Things (IoMT)and swarm learning. Key findings include the identification of prolific authors, influential journals, and significant collaborative networks, with China and India emerging as major contributors. These insights underscore the multidisciplinary nature of swarm intelligence in healthcare, positioning it as a potential game-changer in medical diagnostics and patient care through collaborative and innovative research.

THE BIBLIOMETRIC NETWORK TO IDENTIFY RESEARCH TRENDS IN MULTI-INPUT TRANSFER FUNCTION

Bibliometrics has become one of the most widely used approaches to examining academic publications. Bibliometric analysis helps researchers obtain references from other researchers, strengthening recent research. In a bibliometric analysis, VOSviewer made the analysis process easier. VOSviewer is used to build and visualize bibliometric networks. This study aims to present and visualize an overview of the development of multi-input transfer function research trends and collaboration networks. The research method used is a literature review from Scopus using VOSviewer in 2007-2023. This study shows a growing trend in multi-input transfer function research, with China leading in publications. The development map of the multi-input transfer function is based on nine co-words and ten co-author clusters. The study's results are dominated by applying the model to real data. However, the estimation of multi-input transfer function parameters has yet to be carried out using a numerical approach.

The role of collaborative research network on E-waste studies in North America using a bibliometric approach

Electronic waste (E-waste) has been extensively investigated by researchers throughout the globe, however specific research trends in North America (NA) are yet unknown. This study attempts to present an up-to-date bibliometric view of the e-waste studies, and to identify the research sub-fields and networks. A total of 370 documents were retrieved from the WoS database (from 2005 to 2023). E-waste research topic that originated from NA, as well as its relative growth rate, collaborative measures, institutional productivity, and the popular journals were evaluated in this bibliometric analysis. The study showed that research productivity increased noticeably in the last decade. The USA has the most e-waste publications (72.70%), followed by Canada (22.70%) and Mexico (9.19%). The topic with the highest percentage of papers (209, or 56.49%) was environmental science. Resources Conservation and Recycling was the most active journal on e-waste research. The University of California, USA (8.64%) played an active role in the publication. Author's keywords can be generally grouped into four clusters. Network analysis results suggest that collaborative activities by leading universities will likely boost the number of studies. A diversified and strong collaborative research network may be a key driver for e-waste research output, but not necessarily research impact. It is recommended that policy makers and funding agencies create collaborative funding programs specifically for e-waste research.

Finding the sweet spot for insider action research

ABSTRACT This paper, delivered as a provocative keynote address to the Collaborative Action Research Network (CARN) conference in Manchester on 28th October 2023, explores the question if action research might have a sweet spot. A sweet spot is defined as an ideal location, area or combination of factors for a particular activity or purpose. It draws on the work of Edgar Schein who has argued that the essence of understanding and working with human systems is to focus on the process of how we relate rather than the structure of the relationship. Schein distinguished different levels of relationship and his framing of humble inquiry as a disposition and a skill – a disposition of accessing one’s ignorance in a given setting and a skill of collaborative conversation. The paper proposes that how collaborative relationships that are built, developed and sustained in action research through humble inquiry constitute its sweet spot.

Southern creative robotics – A collaborative framework to implement computational design, fabrication and robotics

Scientific knowledge, ideally neutral and impartial, is inevitably shaped by geographical, economic, and cultural contexts. This research contends that overcoming the constraints of human and economic scarcity, inertia, and limited funding access demands development of collaborative research networks. To this end, four university laboratories from Brazil and Chile - UFRJ, UBB, UTFSM and UAI - have united to advance robotics applied to architecture. The methodology begins with an analysis of Industry 4.0, Fab Lab implementations, and robotics in architecture in the region. They identify key research aspects by mapping each laboratory’s activities and technologies to pinpoint expertise and potential collaborative areas. The authors propose a summary table comparing the labs and a chronological overview to track regional robotics advancement. To raise awareness among peers, the initiative involves joint actions such as courses, workshops and technical visits. 1 Recognising the scarcity of robotic units and the inapplicability of procedures from wealthier contexts, the authors draw on their lab experience to propose guidelines for implementing robotics research units in academia within the region. This includes technological alternatives, installation considerations, and detailed configuration reviews. The deployment of additional robotic units is a means to foster collaboration and bolster the network. Anticipated outcomes encompass increased critical mass, collaborative research initiatives, faculty and student exchange across institutions, higher publication rates, knowledge acquisition, and improved access to global funding agencies. In summary, the Southern Creative Robotics network initiative aims to catalyse the creative integration of digital manufacturing and robotics technology within design in the Ibero-American context.

Real-World Research on Retinal Diseases Using Health Claims Database: A Narrative Review.

Real-world data (RWD) has emerged as a crucial component in understanding and improving patient outcomes across various medical conditions, including retinal diseases. Health claims databases, generated from healthcare reimbursement claims, offer a comprehensive source of RWD, providing insights into patient outcomes, healthcare utilization, and treatment effectiveness. However, the use of these databases for research also presents unique challenges. This narrative review explores the role of real-world research on retinal diseases using health claims databases, highlighting their advantages, limitations, and potential contributions to advancing our understanding and management of the diseases. The review examines the applications of health claims databases in retinal disease research, including epidemiological studies, comparative effectiveness and safety analyses, economic burden assessments, and evaluations of patient outcomes and quality of care. Previous findings demonstrate the value of these databases in generating prevalence and incidence estimates, identifying risk factors and predictors, evaluating treatment effectiveness and safety, and understanding healthcare utilization patterns and costs associated with retinal diseases. Despite their strengths, health claims databases face challenges related to data limitations, biases, privacy concerns, and methodological issues. Accordingly, the review also explores future directions and opportunities, including advancements in data collection and analysis, integration with electronic health records, collaborative research networks and consortia, and the evolving regulatory landscape. These developments are expected to enhance the utility of health claims databases for retinal disease research, resulting in more comprehensive and impactful findings across diverse retinal disorders and robust real-world insights from a large population.

GRAPPA 2023 Collaborative Research Network Meeting.

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) Collaborative Research Network (CRN)/research committee met during the GRAPPA 2023 annual meeting. Updates were provided on GRAPPA research projects, including the Axial Involvement in Psoriatic Arthritis (AXIS), Axial Psoriatic Arthritis Molecular and Clinical Characterisation Study, Diagnostic Ultrasound Enthesitis Tool (DUET), and Sex- and Gender-Based Analysis of the Effectiveness of Advanced Therapies (SAGE) studies, as well as the Health Initiatives in Psoriasis and Psoriatic Arthritis Consortium European States (HIPPOCRATES) and Elucidating the Landscape of Immunoendotypes in Psoriatic Skin and Synovium (ELLIPSS) studies. The highlight of the meeting was a presentation and discussion on the use of digital tools to study psoriatic disease.

All-cause, cardiovascular, and respiratory mortality and wildfire-related ozone: a multicountry two-stage time series analysis

Wildfire activity is an important source of tropospheric ozone (O3) pollution. However, no study to date has systematically examined the associations of wildfire-related O3 exposure with mortality globally. We did a multicountry two-stage time series analysis. From the Multi-City Multi-Country (MCC) Collaborative Research Network, data on daily all-cause, cardiovascular, and respiratory deaths were obtained from 749 locations in 43 countries or areas, representing overlapping periods from Jan 1, 2000, to Dec 31, 2016. We estimated the daily concentration of wildfire-related O3 in study locations using a chemical transport model, and then calibrated and downscaled O3 estimates to a resolution of 0·25° × 0·25° (approximately 28 km2 at the equator). Using a random-effects meta-analysis, we examined the associations of short-term wildfire-related O3 exposure (lag period of 0-2 days) with daily mortality, first at the location level and then pooled at the country, regional, and global levels. Annual excess mortality fraction in each location attributable to wildfire-related O3 was calculated with pooled effect estimates and used to obtain excess mortality fractions at country, regional, and global levels. Between 2000 and 2016, the highest maximum daily wildfire-related O3 concentrations (≥30 μg/m3) were observed in locations in South America, central America, and southeastern Asia, and the country of South Africa. Across all locations, an increase of 1 μg/m3 in the mean daily concentration of wildfire-related O3 during lag 0-2 days was associated with increases of 0·55% (95% CI 0·29 to 0·80) in daily all-cause mortality, 0·44% (-0·10 to 0·99) in daily cardiovascular mortality, and 0·82% (0·18 to 1·47) in daily respiratory mortality. The associations of daily mortality rates with wildfire-related O3 exposure showed substantial geographical heterogeneity at the country and regional levels. Across all locations, estimated annual excess mortality fractions of 0·58% (95% CI 0·31 to 0·85; 31 606 deaths [95% CI 17 038 to 46 027]) for all-cause mortality, 0·41% (-0·10 to 0·91; 5249 [-1244 to 11 620]) for cardiovascular mortality, and 0·86% (0·18 to 1·51; 4657 [999 to 8206]) for respiratory mortality were attributable to short-term exposure to wildfire-related O3. In this study, we observed an increase in all-cause and respiratory mortality associated with short-term wildfire-related O3 exposure. Effective risk and smoke management strategies should be implemented to protect the public from the impacts of wildfires. Australian Research Council and the Australian National Health and Medical Research Council.

Epidemiological Trends and Outcomes of Unicompartmental Knee Arthroplasty Among 104 Million Patients in the United States

BackgroundUnicompartmental knee arthroplasty (UKA) is an alternative to total knee arthroplasty (TKA) for localized osteoarthritis. Recent advancements in UKA implant design and expanding patient criteria may have increased its utilization. However, few studies have examined the use of UKA in the United States. Thus, this study assessed the current and projected future trends of UKA and robotic UKA in the United States through 2035, along with postoperative outcomes. MethodsA collaborative healthcare research network was queried to identify patients who had undergone UKA. Primary outcomes measured included prevalence (P), incidence proportion (IP), and incidence rate (IR) from 2012 to 2022. Chi-squared analyses were done to compare outcomes across categorical data. Regression modeling was performed to project UKA to the year 2035. Statistical significance was held at P < .05 for all analyses. ResultsIn 2022, 1,662 UKAs were performed within the network, a 590% increase from 2012 (241 performed). The IP increased on an average annual basis by 41.8%, the IR by 50%, and the P by 51.3%. A year following UKA, conversion to TKA was the most common orthopaedic complication (39.9%). As of 2022, there were 68 robotic UKAs performed, a 518% increase from the 11 performed in 2012. Regression analysis for UKA through 2035 showed that IP will be 0.04%, IR will be 1.75 × 10−6 cases/person-day, and P will be 0.3%. ConclusionsThese findings are consistent with prior studies indicating a higher utilization of UKA over the past decade. Reported complications were not uncommon, as nearly 40% of patients required a conversion to a TKA. Further research is needed to optimally identify criteria for appropriate patients and determine the benefits robotic UKA may provide, specifically reducing the risk of conversion to a TKA.

Bringing Global Ecological Research to Undergraduate Students Through Citizen Science

AbstractResearch integrated into higher education curricula has been shown by numerous studies to be beneficial to undergraduate students. Citizen science provides an alternative to research performed in a lab and is gaining traction as a good choice for integration into classes. The Undergraduate Student Experiences in Citizen Science (USE Cit Sci) research collaboration network is working to help more instructors in higher education adopt citizen science as part of their curriculum by providing training and educational materials. To date, the Network has identified areas of critical need for citizen science to be more readily used in higher education courses and created a clearinghouse of lessons for faculty to use freely. Forthcoming products of the USE Cit Sci network include direct partnerships between educators and citizen science projects in addition to a peer mentoring program. Given the preponderance of ecology citizen science projects available, bringing this educational opportunity to students opens new avenues of pedagogical experiences.

Measuring the Impact of Centralized High-Performance Computing with Research Collaboration Networks

Measuring the Impact of Centralized High-Performance Computing with Research Collaboration Networks

Identifying Risk of Stillbirth Using Machine Learning

(Am J Obstet Gynecol. 2023;229:327:e1–16) A tool that could help identify women at risk for stillbirth would be a welcome addition to clinical practice. Machine learning models, which can look at relationships between variables in large data sets, was investigated in this article. This study’s aim was to refine a machine learning model to predict stillbirth using data available prior to the gestational age of viability (22 to 24 wk). The researchers performed a secondary analysis of data in the Stillbirth Collaborative Research Network (SCRN), a study database of 982 stillbirths and 3000 representative live births from 2006 to 2009. The study included patients who delivered a live or stillborn fetus at &gt;18 weeks’ gestation from 59 hospitals in 5 states (Georgia, Massachusetts, Rhode Island, Texas, and Utah). The data set included 6000 measures collected from interviews with the mother, postmortem pathological exam, cause of death analysis, and medical record Abstractsion. Subsequently 101 risk factor variables were identified and used in the model. These included maternal and biological father demographics, family history, maternal health history, prenatal lab data, fetal genetics, second-trimester screen, prenatal care history, ultrasound, and social determinants of health.