Innovative Research Environment Research Articles

Association Between Glycemic Variability and Persistent Acute Kidney Injury After Noncardiac Major Surgery: A Multicenter Retrospective Cohort Study.

While the relationship between glycemic variability (GV) and acute kidney injury (AKI) has been a subject of interest, the specific association of GV with persistent AKI beyond 48 hours postoperative after noncardiac surgery is not well-established. This retrospective cohort study aimed to describe the patterns of different GV metrics in the immediate 48 hours after noncardiac surgery, evaluate the association between GV indices and persistent AKI within the 7-day postoperative window, and compare the risk identification capabilities of various GV for persistent AKI. A total of 10,937 patients who underwent major noncardiac surgery across 3 medical centers in eastern China between January 2015 and September 2023 were enrolled. GV was characterized using the coefficient of variations (CV), mean amplitude of glycemic excursions (MAGE), and the blood glucose risk index (BGRI). Multivariable logistic regression was used to examine the relationship between GV and AKI. Optimal cutoff values for GV metrics were calculated through the risk identification models, and an independent cohort from the INformative Surgical Patient dataset for Innovative Research Environment (INSPIRE) database with 7714 eligible cases served to externally validate the risk identification capability. Overall, 274 (2.5%) of the 10,937 patients undergoing major noncardiac surgery met the criteria of persistent AKI. Higher GV was associated with an increased risk of persistent AKI (CV: odds ratio [OR] = 1.26, 95% confidence interval [CI], 1.08-1.46; MAGE: OR = 1.31, 95% CI, 1.15-1.49; BGRI: OR = 1.18, 95% CI, 1.08-1.29). Compared to models that did not consider glycemic factors, MAGE and BGRI independently contributed to predicting persistent AKI (MAGE: areas under the curve [AUC] = 0.768, P = .011; BGRI: AUC = 0.764, P = .014), with cutoff points of 3.78 for MAGE, and 3.02 for BGRI. The classification of both the internal and external validation cohorts using cutoffs demonstrated good performance, achieving the best AUC values of 0.768 for MAGE in the internal cohort and 0.777 for MAGE in the external cohort. GV measured within 48 hours postoperative period is an independent risk factor for persistent AKI in patients undergoing noncardiac surgery. Specific cutoff points can be used to stratify at-risk patients. These findings indicate that stabilizing GV may potentially mitigate adverse kidney outcomes after noncardiac surgery, highlighting the importance of glycemic control in the perioperative period.

KYRILLOMETHODIKON – an Innovative Research Environment for Shared Access to Scientific Content

The Cyrillo-Methodian heritage is a common phenomenon of European value, promoting religious tolerance, cultural equality, and understanding of European cultural history and identity. The virtual cluster KYRILLOMETHODIKON developed at the Cyrillo-Methodian Research Centre (CMRC) at BAS is part of the Centre of Excellence "Heritage BG" (BG05M2OP001-1.001-0001 Project), aiming to provide structured knowledge on this phenomenon and its manifestations. The digital repository offers resources for research on the first stage of Slavic Christian culture with an accent on its Old Bulgarian part, reflecting the multidisciplinary research paradigm of the Centre: studies in medieval literature, linguistics, history, theology, art history, and anthropology. To ensure the educational socialisation of the research in these disciplines, a Scientific and Educational Interdisciplinary Centre for Cyrillo-Methodian studies was created within the framework of KYRILLOMETHODIKON, sharing educational tools for a wide range of audiences, including children, school students, undergraduates, and doctoral candidates.

EDITORIAL

In the dynamic realm of academic publishing, embracing change is crucial. Beginning in its issue 46.1, International Sports Studies (ISS) is transitioning from a subscription-based model to a fully open-access platform, enhancing inclusivity and scholarly collaboration. This shift addresses the barriers posed by traditional paywalls, enabling unrestricted access to high-quality research for all, especially those from underfunded institutions and developing nations. The new model emphasizes transparency, replicability, and public engagement in scientific research. To sustain rigorous peer review under open access, ISS has implemented a socialized Article Processing Charge (APC) system and substantial discount programs for scholars from low-income regions. This transition aligns with global trends toward open science, fostering a collaborative and innovative research environment. ISS extends gratitude to its editorial team and reviewers, whose efforts are vital in this transformative journey, heralding a new era dedicated to the free flow of knowledge.

Key Factors in Boosting the Efficiency of Scientific Research Administration

The key factors to improve the efficiency of scientific research management include: clear goal setting and strategic planning to ensure the orderly and efficient scientific research work; optimizing resource allocation, material and financial resources to avoid resource waste; strengthening the construction of scientific research team, improving the quality of scientific researchers, team cohesion and innovation ability; establishing scientific evaluation and incentive mechanism to stimulate the enthusiasm of scientific researchers and promote the output of scientific research results; promoting information and digital management, and using modern technology to improve management efficiency and quality. By comprehensively applying these pivotal factors, the standard of scientific research management can undergo a marked enhancement. This comprehensive approach not only ensures the efficient allocation of resources but also fosters a collaborative and innovative research environment. The optimization of management systems and processes leads to increased efficiency and productivity, while the emphasis on team collaboration and communication fosters a stronger sense of unity and purpose among researchers.

Open datasets in perioperative medicine: a narrative review.

With the growing interest of researchers in machine learning and artificial intelligence (AI) based on large data, their roles in medical research have become increasingly prominent. Despite the proliferation of predictive models in perioperative medicine, external validation is lacking. Open datasets, defined as publicly available datasets for research, play a crucial role by providing high-quality data, facilitating collaboration, and allowing an objective evaluation of the developed models. Among the available datasets for surgical patients, VitalDB has been the most widely used, with the Medical Informatics Operating Room Vitals and Events Repository recently launched and the Informative Surgical Patient dataset for Innovative Research Environment expected to be released soon. For critically ill patients, the available resources include the Medical Information Mart for Intensive Care, the eICU Collaborative Research Database, the Amsterdam University Medical Centers Database, and the High time Resolution ICU Dataset, with the anticipated release of the Intensive Care Network with Million Patients' information for the AI Clinical decision support system Technology dataset. This review presents a detailed comparison of each to enrich our understanding of these open datasets for data science and AI research in perioperative medicine.

Application of model for end-stage liver disease as disease classification in cardiac valve surgery: a retrospective study based on the INSPIRE database.

Model for end-stage liver disease (MELD) is an effective predictive marker for renal, hepatic, and cardiac dysfunctions. In this study, we explore the correlation between MELD scores and the outcomes of patients undergoing cardiac valve surgery. We conducted a retrospective analysis of clinical data from patients who underwent cardiac valve surgery, encompassing procedures on the aortic valve, mitral valve, and tricuspid valve, using the Informative Surgical Patient dataset for Innovative Research Environment (INSPIRE) database, we conducted receiver operating characteristic (ROC) analyses on the study participants and chose MELD as the primary scoring tool for our study due to its optimal area under the curve (AUC), patients were stratified into high (MELD ≥18) and low (MELD <18) groups based on the determined cutoff value. The perioperative clinical data of the two groups were compared. The analysis revealed 751 patients in the low MELD group (75.5%) and 244 patients (24.5%) in the high MELD group. Patients in the high MELD group exhibited a lower body mass index (BMI) compared to those in the low MELD group. In comparison to the low MELD group, the high MELD group exhibited a higher rate of emergency surgery (10.66% vs. 5.99%, P=0.01), along with prolonged anesthesia time, surgery time, and cardiopulmonary bypass (CPB) time. Regarding clinical prognosis, the high MELD group demonstrated a higher 28-day mortality rate (10.66% vs. 0.8%, P<0.001), as also observed in the analysis of three valve subgroups. Additionally, the high MELD group experienced longer hospitalization and intensive care unit (ICU) stay, and a higher proportion of patients requiring mechanical circulatory support, including intra-aortic balloon pump (IABP) assist (14.75% vs. 3.86%, P<0.001), extracorporeal membrane oxygenation (ECMO) assist (7.38% vs. 0.8%, P<0.001), and continuous renal replacement therapy (CRRT) (27.87% vs. 1.46%, P<0.001) post-surgery. The Kaplan-Meier survival curves illustrated a significantly lower mortality rate in the low MELD group compared to the high MELD group, with highly significant statistical differences (P<0.001). The MELD score demonstrates a robust predictive value for clinical outcomes following cardiac valve surgery, underscoring its utility as a viable metric for disease stratification research.

Persistent factors facilitating excellence in research environments

The study presented here identifies robust and time-invariant features that characterise dynamic and innovative research environments. It takes as its point of departure the results of an empirical study conducted in 2002 which identified the common characteristics of 15 dynamic and innovative public research environments, and focusses on their development by revisiting the environments after more than a decade, hence mapping them in the current research landscape. Using a model for studies of research environments that was constructed and used in the Nordic countries, the study maps both internal elements and those in the framework of the environments that influence research performance and identifies persistent factors in dynamic and innovative research environments. The findings add to our understanding of how to improve the overall ecology of knowledge production and create optimal conditions that support research environments in pursuing and ensuring excellence. Implications for further research and policy are discussed.

On‐demand virtual research environments and the changing roles of librarians

PurposeThe aim of this paper is to discuss how new technologies for supporting scientific research will possibly influence the librarians' work.Design/methodology/approachThe discussion is conducted in a context that takes into account the emergence of e‐infrastructures as means to realise a new model of producing, using and sharing information resources and even to change the concept of information resource itself. At the core of this innovation there are virtual research environments, i.e. evolved versions of the current “research libraries”.FindingsThe environments provide scientists with collaborative and customised environments supporting results production and exchange around the globe in a cost‐efficient manner. The experiences made with these innovative research environments within the D4Science project is reported.Originality/valueOn the basis of this experience, possible professional profiles are suggested for librarians working in these new evolved “research libraries”.

Dynamic research environments: a development model

The paper outlines a model for the development of dynamic and innovative research environments in national innovation systems. The development model is based on an in-depth study and analysis of fifteen research environments selected by the Danish Council for Research Policy as examples of dynamic and innovative public research environments. According to the model, research environments follow certain stages with respect to epistemological, organizational and socio-cultural development that either enable them to advance from one stage to the next in the model or impede this advance. The consequence of the latter is either a static state or dissolution. In the long run only dynamic and innovative research environments survive. The analysis of the cases studied identifies several preconditions that have to be present in such research environments in order for them to emerge, develop and finally reach excellence. The results presented here may well be a valid tool for policy-makers, particularly in Europe where the framework conditions for public R&D are comparable to the Danish.

Innovation and dynamics in public research environments in Denmark: a research-policy perspective

The theme for this article is dynamic and innovative research environments and the factors that characterise them. The focus is on the relationship between the organisation and management of research, research processes and environmental conditions on the one hand, and research quality and outcomes on the other. The article takes as a point of departure the results of an empirical study of 15 university and other public-sector research environments in Denmark. It identifies the features that characterise the framework of research, research processes, the working and social milieu of research environments and their influence on research in a smaller European country from a research-policy perspective. Innovative research environments could serve as references for research agents attempting to promote innovation and further develop a dynamic science base. Copyright , Beech Tree Publishing.