Collaborative Research Research Articles

Machine learning models for mortality prediction in critically ill patients with acute pancreatitis-associated acute kidney injury.

The occurrence of acute kidney injury (AKI) was associated with an increased mortality rate among acute pancreatitis (AP) patients, indicating the importance of accurately predicting the mortality rate of critically ill patients with acute pancreatitis-associated acute kidney injury (AP-AKI) at an early stage. This study aimed to develop and validate machine learning-based predictive models for in-hospital mortality rate in critically ill patients with AP-AKI by comparing their performance with the traditional logistic regression (LR) model. This study used data from three clinical databases. The predictors were identified by the Recursive Feature Elimination algorithm. The LR and two machine learning models-random forest (RF) and eXtreme Gradient Boosting (XGBoost)-were developed using 10-fold cross-validation to predict in-hospital mortality rate in AP-AKI patients. A total of 1089 patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD) were included in the training set and 176 patients from Xiangya Hospital were included in the external validation set. The in-hospital mortality rates of the training and external validation sets were 13.77% and 54.55%, respectively. Compared with the area under the curve (AUC) values of the LR model and the RF model, the AUC value of the XGBoost model {0.941 [95% confidence interval (CI) 0.931-0.952]} was significantly higher (both P<.001) and the XGBoost model had the smallest Brier score of 0.039 in the training set. In the external validation set, the performance of the XGBoost model was acceptable, with an AUC value of 0.724 (95% CI 0.648-0.800). However, it did not differ significantly from the LR and RF models. The XGBoost model was superior to the LR and RF models in terms of both the discrimination and calibration in the training set. Whether the findings can be generalized needs to be further validated.

Archetypes of Open Science Partnerships: connecting aims and means in open biomedical research collaborations

Open Science Partnerships (OSPs) are gaining attention as alternatives to university–industry collaborations with restrictive IPR and knowledge sharing policies. OSPs have different expected outcomes and deploy varying means to reach them. Appreciating these differences is crucial to understanding their scientific and socio-economic impact, and yet these differences have never been systematically investigated. This exploratory study draws on qualitative case studies of five biomedical OSPs involving academic partners and pharmaceutical companies. It identifies key elements—purpose, activities and structure—that can be used to describe how OSPs are designed. We identify two key aspects of purpose—predominant intent and research aims—which we argue affect the activities and structure of an OSP. Based on these two aspects, we propose four ideal types of OSPs that are designed to provide a starting point for researchers who explore the nature and impact of OSPs and for practitioners who are developing OSPs and wish to ensure that they deploy appropriate means to meet the intended outcomes of their partnership.

Reimagining LIS Education: Everything We Wanted to Say but Were Afraid To

This panel includes six library and information science (LIS) academics who bring together international perspectives on pedagogy rooted in theoretical framings. The panelists will discuss topics meaningful to the instructor experience including social justice, teaching empathy in the LIS classroom, faculty/student research collaboration, student socialization to the graduate academy, and inclusion of faculty members with a disability.

Research hotspots and trend analysis of embryonic stem cell therapy based on bibliometric analysis

Objectives: This article was designed to deeply analyze the research status and frontier trend of embryonic stem cells (ESCs) therapy, and accurately understand the development stage and characteristics of ESCs therapy research. Methods: By entering keywords, downloading literature, applying bibliometric analysis software, and website, research trends in ESCs therapy from 2015 to 2024 were analyzed, covering annual trends, countries or regions, institutions, and authors. Results: The analysis reveals a general decline in publication volume since 2018, with stabilization observed in recent years, indicating ongoing scholarly interest in ESC research. The United States and China emerged as leading contributors, with China showing rapid growth since 2015. American institutions, particularly Harvard University, lead in publication volume, while the University of Cambridge stands out for its high citation impact. Prominent authors like Meissner Alexander play a central role in advancing the field through extensive collaboration. Key journals, including Cell Stem Cell and Nature, dominate ESC research publications. Conclusions: In the field of ESCs therapy, other countries need to make efforts to keep up with the United States, and try to innovate and expand the contents of stem cell studies. In summary, this article conducts a systematic review and trend outlook of stem cell-related studies, exploring current research trends and hotspots and emphasizing the importance of international collaboration and quality-driven research in ESCs.

Implementation of a common data model in Health Data Research Network Canada: Lessons learned

BackgroundThe Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is a standardized structure for observational data that enables collaborative health research. Health Data Research Network (HDRN Canada) is conducting a pilot study to implement this standard at partner data linkage centers. ObjectiveOur objective is to describe the process used to implement the OMOP CDM, methodological and technical challenges, and the approach to evaluate CDM implementation at multiple sites. ApproachCDM working group members include technical staff from data linkage centers in four provinces and a national data coordinating center, HDRN Canada operational and executive leads, and OMOP CDM consultants. The evaluation aims to collect quantitative and qualitative information on Extract, Transform, and Load (ETL) processes, performance of the CDM in a multi-site observational case study, and data quality. ResultsInitial steps have included collaborative learning about the CDM structure, importing open-source tools and licensed coding standards (i.e., Systematized Nomenclature of Medicine-Clinical Terms [SNOMED-CT], RxNorm) at data linkage centres, and exploring the technical requirements for developing ETL processes and hosting OMOP data. Creation of select OMOP tables has been accompanied by discussions about models for mapping clinical and prescription drug coding standards. A scoping review of evaluation strategies is underway. Conclusions and ImplicationsThe benefits of standardizing health data for multi-site research and supporting the production of findable, accessible, interoperable and reusable (FAIR) national data underscores the importance of HDRN Canada embarking on this OMOP CDM implementation project. The lessons learned will benefit other multi-site data standardization initiatives.

The Impact of Heat Exposure on the Health and Performance of Soccer Players: A Narrative Review and Bibliometric Analysis.

The impact of heat exposure on the health and performance of soccer players is a widely discussed topic. The purpose of this study is to provide a comprehensive overview of the international literature that has addressed this issue. To achieve this objective, we initially conducted a bibliometric analysis and a literature review of the main topics that emerged through bibliometric techniques. For the bibliometric analysis, we employed VOSviewer software (version 1.6.20.0) and used documents found in the Scopus database. The analysis ultimately included 133 documents published in 66 sources. Key journals and authors were identified, highlighting significant contributions to the field. Science mapping revealed collaboration networks and research focus areas such as physical health, safety, soccer performance, dehydration and hydration, physiological mechanisms and monitoring, nutrition, fluid intake, and cooling techniques. Based on the key areas highlighted in the identified clusters, which emerged from the co-occurrence analysis of the author keywords, the following three topics were developed in the literature review: (a) the physiology and health of football players; (b) performance impacts; and (c) strategies to prevent negative consequences. The review showed that high heat exposure can reduce the physical and cognitive performance of athletes and prove detrimental to their health. To mitigate the negative consequences, appropriate hydration strategies, heat acclimatization, and cooling techniques have been proposed. Our findings provide the international scientific community with comprehensive knowledge of the existing literature, laying the foundation for future research while simultaneously offering coaches and athletes the necessary theoretical knowledge to help improve safety and performance.

Linkja: Open Source Privacy-Preserving Record Linkage Tool

ObjectiveTo introduce Linkja, an open-source, certified, Privacy-Preserving Record Linkage (PPRL) tool with the primary objective of facilitating secure and efficient record linkage across diverse datasets, while preserving individual privacy, providing a comprehensive and privacy-centric approach to program assessments. ApproachLinkja has been used to evaluate Chicago’s Flexible Housing Pool program by securely linking various datasets, including Homeless Management Information Systems (HMIS), Electronic Health Records, Jail records, Medical Examiner reports, and Public Health data. Data contributors used separate honest brokers for record linkage, and generation of salt and cryptography keys. ResultsLinkja enabled this multi-sector linkage by facilitating legal and data sharing agreements, avoided licensing fees and procurement processes for all parties, and provided a de-identified for analysis. The insights gleaned from the analysis motivated the FHP program to plan a follow-up evaluation that will add emergency serviced records from a separate City of Chicago department. The successful linkage provided a nuanced understanding of the association between stable housing and reduced healthcare utilization. ConclusionsLinkja's cryptographic certification ensures the highest standards of security during data linkage, safeguarding sensitive information. The matching rules, validated on hard-to-match populations, prove essential in maintaining accuracy and reliability in the linkage process. ImplicationsThe open-source nature of Linkja promotes transparency and collaboration in the development and enhancement of privacy-preserving record linkage techniques. This tool has broad implications for sectors dealing with sensitive data, fostering responsible data-sharing practices, and enabling secure collaborative research initiatives without compromising individual privacy.

Advancements in Pan-Canadian Data Access and Analysis Facilitation: Insights from Collaborative Health Research supported in Alberta, British Columbia, and Ontario

ObjectiveTo highlight progress in facilitating and supporting pan-Canadian data analysis research that is supported by a central coordinating center. This process will be illustrated with the use of a recently completed project that has been supported using data from Alberta, British Columbia, and Ontario. ApproachThe project represents a significant endeavor within the central coordinating center, as it necessitates coordination for data access, data importation and analytical support across three provinces. The focus will be on the administrative processes refined to support collaborative research endeavors. While specific project details will remain undisclosed, procedural enhancements within the central coordinating center framework will be highlighted. Key discussion points include standardized protocols for data access, collaborative efforts to streamline data importation, and facilitation of analytical support across jurisdictions. The type of cross-jurisdictional analytic support will as well be highlighted; data variables harmonization and sharing of data algorithms cohorts across the participating provincial data centers in support of the meta-analysis. ResultsThe successful completion of the final data analysis underscores the effectiveness of a unified data access coordination center for researchers seeking multi-regional health data in Canada. By highlighting advancements enabled by the central coordinating center and provincial data centers, this submission aims to inform researchers about the current landscape of pan-Canadian research and foster opportunities for future collaboration. ImplicationsSharing insights and lessons from this project emphasizes the advancements facilitated by the central coordinating center and provincial data centers, informing researchers about the potential of pan-Canadian research, and encouraging future collaborative endeavors.

Assessing the effect of anthocyanins through diet and supplementation on cognitive function in older adults at risk for dementia: protocol for a randomised controlled trial

IntroductionPromising evidence is emerging for the procognitive, anti-inflammatory and neuroprotective properties of dietary flavonoids, particularly anthocyanins that provide red, purple and blue plant pigments.Methods and analysisThe ‘Food for Thought’ study...

Equity in practice: Lessons from a cohort-based model for community voice and input in data linkage and use

Objective and ApproachCross-sector data sharing and linkage can transform information about individuals into actionable intelligence to build stronger, healthier, and more just communities. Yet, the use of cross-sector data can also reinforce legacies of racist policies and produce inequitable resource allocation, access, and outcomes. To avoid this, we must embed data equity practices throughout the data life cycle and provide mechanisms for community voice and input. However, few integrated data systems are doing this well. To combat this challenge, AISP designed a 30-month technical assistance program–the Equity in Practice Learning Community (EiPLC)--to collaboratively develop guidance and models for centering racial equity in data integration. This session provides an overview of our EiPLC Scope &amp; Sequence, a curriculum we designed to guide this work. Currently, 10 jurisdictions in the U.S. are receiving coaching around this curriculum and shifting their data integration practices to advance equity. ResultsTo date, progress has been nonlinear, with positive results. Work in action across the cohorts includes changes to cross-agency data governance, collaborative research agendas, legal agreements, staffing, and community participation practices including. ConclusionsWhile these efforts are nascent, progress is evident and site team members indicate growth as individuals and as site teams. Key features of success include a focus on building relationships, establishing working norms, grappling with racialized histories, and interrogating the role of structural racism and systems of power. ImplicationsThe EiPLC provides a successful model for supporting sites to embed data equity principles throughout the data life cycle.

The vision of the circular factory for the perpetual innovative product

Abstract The growing scarcity of global resources demands a transition from linear to circular production patterns. This article presents a novel vision for integrating linear and circular production processes within a flexible and autonomous production system to achieve perpetual product use. The approach aims to preserve the added value of products and to integrate the design of product generations and production systems. Within the circular factory, the following core aspects must be examined: predicting functions of products, managing uncertainty in used products and process sequences, learning human action for complex tasks, implementing changeable, autonomous production systems, and enabling knowledge modeling for the circular factory across domains. Aspired results are a design for circular factory, effective strategies for uncertainty management and autonomous systems adaptation as well as the externalization of operational knowledge. This research is part of the Collaborative Research Center (CRC) 1574, which explores these aspects in detail. For an in-depth understanding of specific components, it is referred to other publications by the CRC 1574.

Interactions of multiple stressors on the Bombay-duck Harpadon nehereus population in a complex estuarine ecosystem

In an era marked by unprecedented anthropogenic change, marine systems are increasingly subjected to interconnected and dynamic external stressors, which profoundly reshape the behavior and resilience of marine ecological components. Nevertheless, despite widespread recognition of the significance of stressor interactions, there persist notable knowledge deficits in quantifying their interactions and the specific biological consequences that result. To bridge this crucial gap, this research detected and examined the causal relationships between five key exogenous stressors in a complex estuarine ecosystem. Furthermore, a Bayesian Hierarchical Spatio-temporal modeling framework was proposed to quantitatively evaluate the distinct, interactive, and globally sensitive effects of multiple stressors on the population dynamics of a crucial fish species: Harpadon nehereus. The results showed that interactions were detected between fisheries pressure (FP), the Pacific Decadal Oscillation index (PDO), runoff volume (RV), and sediment load (SL), with five of these interactions producing significant synergistic effects on H. nehereus biomass. The SL*PDO and RV*PDO interactions had positive synergistic effects, albeit through differing processes. The former interaction amplified the individual effects of each stressor, while the latter reversed the direction of the original impact. Indeed overall, the synergistic effect of multiple stressors was not favorable, with FP in particular posing the greatest threat to H. nehereus population. This threat was more pronounced at high SL or negative PDO phases. Therefore, local management efforts aimed at addressing multiple stressors and protecting resources should consider the findings. Additionally, although the velocity of climate change (VoCC) failed to produce significant interactions, changes in this stressor had the most sensitive impacts on the response of H. nehereus population. This research strives to enhance the dimensionality, generalizability, and flexibility of the quantification framework for marine multi-stressor interactions, aiming to foster broader research collaboration and jointly tackle the intricate pressures facing marine ecosystems.

The Greening of One Health: Plants, Pathogens, and the Environment.

One Health has an aspirational goal of ensuring the health of humans, animals, plants, and the environment through transdisciplinary, collaborative research. At its essence, One Health addresses the human clash with Nature by formulating strategies to repair and restore a (globally) perturbed ecosystem. A more nuanced evaluation of humankind's impact on the environment (Nature, Earth, Gaia) would fully intercalate plants, plant pathogens, and beneficial plant microbes into One Health. Here, several examples point out how plants and plant microbes are keystones of One Health. Meaningful cross-pollination between plant, animal, and human health practitioners can drive discovery and application of innovative tools to address the many complex problems within the One Health framework.

Bridging Humanities and Science Education : The SCISSORS Project

As a reader, interested in education, you know that science education is more than just teaching scientific concepts and theories. It is about nurturing the students’ curiosity, motivation, and critical thinking skills. However, have you ever considered the connection between science and social competencies? This is where our innovative approach comes in. The purpose of this ebook is to lay the pedagogical foundation for an educational framework combining the sciences and the humanities, and achieve impact through collaborative research and educational activities. Designed for educators, principals of educational institutions and decision-makers seeking to implement a novel pedagogical program, this resource presents valuable materials. Through exploring research-based insights and good practices from Hungary, and Finland, the primary goal is to influence the perspective of teachers regarding the instruction of sciences combined with humanities. This ebook advocates for a paradigm shift where soft skills and critical, analytical thinking are not seen in contrast but as interdependent, fostering a symbiotic relationship for a more impactful and holistic education. Our educational framework offers a pathway for integrating the development of social competencies into the existing structures of diverse school systems, eliminating the necessity for additional staff. Implementing our approach is a preventative measure against burnout for science teachers and other educators and fosters creativity and self-reflection throughout the teaching and learning journey. This method empowers teachers to link natural scientific discoveries with social occurrences, rendering them tangible and comprehensible for students.

Directions for the development of higher education aimed at stimulating students' creative abilities and self-education in scientific research: developing a model of interpersonal learning

In academic education, particularly in the context of collaborative research, the interaction between academic faculty, students, and scholars through electronic information media presents both innovative opportunities and challenges. While these technologies offer enhanced learning methods, they also raise concerns about the erosion of interpersonal connections, which are crucial to the educational process. This study aims to explore the dynamics of informational self-education and its impact on both faculty and students, recognizing the dual influence of positive and negative factors. Academic learning and collaborative research are complex processes that encompass cognitive, motivational, emotional, and behavioral dimensions, all of which are influenced by economic and financial mechanisms. The primary goal of this research is to identify and develop a model of interpersonal learning that can enhance the effectiveness of higher education. Through a comprehensive analysis, the study concludes that while informational self-education offers new avenues for improving the quality of academic teaching, the role of interpersonal learning, particularly when supported by advanced information technologies, is of paramount importance. This study underscores the need for a balanced approach that integrates the benefits of informational self-education with the essential human elements of interpersonal learning to foster a more effective and holistic educational experience.

Visualizing Scholarly Trends in Stochastic Models for Disease Prediction.

Stochastic models play a pivotal role in disease prediction by accounting for randomness and uncertainty in biological systems. This study offers a visualization of trends in the application of stochastic models for disease prediction from 1990 to 2024, based on a bibliometric analysis of Scopus data. Key findings reveal a significant growth in research post-2014, largely driven by global health challenges like COVID-19. Despite these advancements, gaps remain in applying these models to non-communicable diseases and low-resource settings. By integrating computational techniques like machine learning, stochastic models hold promise for improving predictive accuracy. This study highlights the need for further international collaboration and interdisciplinary research, offering practical insights for researchers and public health professionals aiming to enhance disease prediction and intervention strategies.

ThP7.2 - Nonoperative versus Operative Management in Traumatic Liver Injuries: A Prospective LiPTA Multicentre Study

Abstract Aims The liver is the most injured abdominal organ, with subsequent significant morbidity and mortality. Over the last 30-years liver trauma management has changed, with an increase in nonoperative versus operative approaches. However, contemporary UK data on how liver trauma is managed is limited. Methods This prospective, multicentre audit was conducted through the National Trauma Research and Innovation Collaborative at 18 participating major trauma centres and units across the UK between 01/01/2021-01/10/2021. Differences in demographics, physiology, injury mechanisms, complications, length of stay, and 30-day mortality were compared between patients undergoing definitive nonoperative, including interventional radiology, or operative management. Univariable tests were used to compare data where appropriate with adjusted odds ratios of 30-day mortality used to assess definitive management via a multivariable model. Results Of 266 patients, 76.69% underwent nonoperative management. Operative patients were significantly younger, median age 29 (IQR 20-41) vs. 34 (IQR 23-52) p=0.0146, sustained more penetrating compared to blunt injuries (62.90% vs. 9.80%, p&amp;lt;0.0001), had more active bleeding on CT (32.65% vs. 5.97%, p&amp;lt;0.0001), required massive transfusions (27.42% vs. 8.33%, p=0.0001), sustained more inpatient complications (29.03% vs. 1.96%, p&amp;lt;0.001) and more physiologically unstable on admission. Operative compared to nonoperative management resulted in a significant 20-fold increase in 30-day mortality, (aOR 20.21, 95%CI 3.28-124.43, p=0.001). Conclusions These findings highlight the current approach to UK liver trauma management. Patients undergoing operative management have significantly greater morbidity and mortality yet may represent the severest of liver injures. Where appropriate nonoperative management should be used following liver trauma to reduce this.

The past, present and future of multi-scale modelling applied to wave-structure interaction in ocean engineering.

Concepts and evolution of multi-scale modelling from the perspective of wave-structure interaction have been discussed. In this regard, both domain and functional decomposition approaches have come into being. In domain decomposition, the computational domain is spatially segregated to handle the far-field using potential flow models and the near field using Navier-Stokes equations. In functional decomposition, the velocity field is separated into irrotational and rotational parts to facilitate identification of the free surface. These two approaches have been implemented alongside partitioned or monolithic schemes for modelling the structure. The applicability of multi-scale modelling approaches has been established using both mesh-based and meshless schemes. Owing to said diversity in numerical techniques, massively collaborative research has emerged, wherein comparative numerical studies are being carried out to identify shortcomings of developed codes and establish best-practices in numerical modelling. Machine learning is also being applied to handle large-scale ocean engineering problems. This paper reports on the past, present and future research consolidating the contributions made over the past 20 years. Some of these past as well as future research contributions have and shall be actualized through funding from the Newton International Fellowship as the next generation of researchers inherits the present-day expertise in multi-scale modelling. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

Navigating post-pandemic challenges through institutional research networks and talent management

Institutions actively seek global talent to foster innovation in the contemporary landscape of scientific research, education, and technological progress. The COVID-19 pandemic underscored the importance of international collaboration as researchers and academicians faced limitations in accessing labs and conducting research experiments. This study uses a research collaboration system to examine the relationship between organizational intellectual capital (Human and structural Capital) and team scientific and technological performance. Further, this study underscores the moderating role of top management support. Using a time-lagged study design, data were collected from 363 participants in academic and research institutions. The results show a positive relationship between organizational intellectual capital (Human and structural Capital) and team scientific and technological performance using a research collaboration system. Moreover, top management support positively moderates the study’s hypothesized relationships. The study’s findings contribute significantly to existing knowledge in this field, with implications for academia, researchers, and government focused on technology transmission, talent management, research creative collaboration, supporting innovation, scientific research, technological progress, and preparing for future challenges.

Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills.

Health professional education stands to gain substantially from collective efforts toward building video databases of skill performances in both real and simulated settings. An accessible resource of videos that demonstrate an array of performances - both good and bad-provides an opportunity for interdisciplinary research collaborations that can advance our understanding of movement that reflects technical expertise, support educational tool development, and facilitate assessment practices. In this paper we raise important ethical and legal considerations when building and sharing health professions education data. Collective data sharing may produce new knowledge and tools to support healthcare professional education. We demonstrate the utility of a data-sharing culture by providing and leveraging a database of cardio-pulmonary resuscitation (CPR) performances that vary in quality. The CPR skills performance database (collected for the purpose of this research, hosted at UK Data Service's ReShare Repository) contains videos from 40 participants recorded from 6 different angles, allowing for 3D reconstruction for movement analysis. The video footage is accompanied by quality ratings from 2 experts, participants' self-reported confidence and frequency of performing CPR, and the demographics of the participants. From this data, we present an Automatic Clinical Assessment tool for Basic Life Support that uses pose estimation to determine the spatial location of the participant's movements during CPR and a deep learning network that assesses the performance quality.