Factors influencing knowledge sharing between scientific specialists in knowledge networks and communities of practice: A systematic literature review

Knowledge sharing is a competitive advantage and necessity for the success of any organization. Meanwhile, knowledge networks have been introduced as a way to enhance knowledge sharing between individuals and as an effective tool to facilitate knowledge exchange in clinical, educational, and commercial fields. The purpose of this paper is to identify the factors that can affect the level of knowledge sharing and exchange between academic and scientific specialists in knowledge networks and Communities of Practice (COP). A systematic literature review was conducted using the PRISMA guidelines. Four databases were searched, including Scopus, Web of Science, PubMed, and ProQuest. Google Scholar search was conducted to complete the search and ensure the tracking of the gray literature. Also, relevant sources, references, and reference lists of the related articles were reviewed. The studies were searched from April until August 2022 and finally the content analysis of the findings was done. Two reviewers independently assessed the quality of included studies. Data were extracted using the Joanna Briggs Institute (JBI) checklist tool. Of the 1439 records, 13 studies met the inclusion criteria. This study identified three main categories of factors affecting knowledge sharing in knowledge networks and COPs as individual factors, organizational, and structural. The results showed that knowledge networks provide opportunities to overcome professional barriers and complex systemic challenges and lead to knowledge sharing and exchange among scientific specialists. This article has important implications for managers, health policymakers, and academics who wish to expand knowledge sharing of scientific specialists through knowledge networks and CoPs in knowledge-based organizations.

CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development.

A Connected World: System-Level Support Through Biosensors

The goal of protecting the health of future generations is a blueprint for future biosensor design. Systems-level decision support requires that biosensors provide meaningful service to society. In this review, we summarize recent developments in cyber physical systems and biosensors connected with decision support. We identify key processes and practices that may guide the establishment of connections between user needs and biosensor engineering using an informatics approach. We call for data science and decision science to be formally connected with sensor science for understanding system complexity and realizing the ambition of biosensors-as-a-service. This review calls for a focus on quality of service early in the design process as a means to improve the meaningful value of a given biosensor. We close by noting that technology development, including biosensors and decision support systems, is a cautionary tale. The economics of scale govern the success, or failure, of any biosensor system.

Hierarchical Structure of Depression Knowledge Network and Co-word Analysis of Focus Areas

Contemporarily, depression has become a common psychiatric disorder that influences people's life quality and mental state. This study presents a systematic review analysis of depression based on a hierarchical structure approach. This research provides a rich theoretical foundation for understanding the hot spots, evolutionary trends, and future related research directions and offers further guidance for practice. This investigation contributes to knowledge by combining robust methodological software for analysis, including Citespace, Ucinet, and Pajek. This paper employed the bibliometric methodology to analyze 5,000 research articles concerning depression. This current research also employed the BibExcel software to bibliometrically measure the keywords of the selected articles and further conducted a co-word matrix analysis. Additionally, Pajek software was used to conduct a co-word network analysis to obtain a co-word network diagram of depression. Further, Ucinet software was utilized to calculate K-core values, degree centrality, and mediated centrality to better present the research hotspots, sort out the current status and reveal the research characteristics in the field of depression with valuable information and support for subsequent research. This research indicates that major depressive disorder, anxiety, and mental health had a high occurrence among adolescents and the aged. This present study provides policy recommendations for the government, non-governmental organizations and other philanthropic agencies to help furnish resources for treating and controlling depression orders.

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

The Evolution of Publication Hotspots in Electronic Health Records from 1957 to 2016 and Differences Among Six Countries

This study aims to reveal the evolution of publication hotspots in the field of electronic health records (EHRs) and differences among countries. We applied keyword frequency analysis, keyword co-occurrence analysis, principal component analysis, multidimensional scaling analysis, and visualization technology to compare the high-frequency Medical Subject Heading (MeSH) terms in six countries during the periods 1957-2008 and 2009-2016. After 2009, the number of MeSH terms reflecting information exchange and information mining increased, and various types of evaluations based on EHRs and cohort studies significantly increased. The top 20 MeSH terms between 2009 and 2016 constitute five relatively larger knowledge groups. Thus, we conclude that publication hotspots in EHR field have shifted from issues related to the adoption of EHRs to the utilization of EHRs, and the knowledge structure has become systematic. The publication's focus was different in the six countries, which may relate to their national characteristics.

KnetMaps: a BioJS component to visualize biological knowledge networks

KnetMaps is a BioJS component for the interactive visualization of biological knowledge networks. It is well suited for applications that need to visualise complementary, connected and content-rich data in a single view in order to help users to traverse pathways linking entities of interest, for example to go from genotype to phenotype. KnetMaps loads data in JSON format, visualizes the structure and content of knowledge networks using lightweight JavaScript libraries, and supports interactive touch gestures. KnetMaps uses effective visualization techniques to prevent information overload and to allow researchers to progressively build their knowledge.

Annotating gene sets by mining large literature collections with protein networks

Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts. To evaluate this system, we predict functions for gene sets representing known pathways and find that our approach achieves substantial improvement over the conventional text-mining baseline method. Moreover, our system discovers novel annotations for gene sets or pathways without previously known functions. Two case studies demonstrate how the system is used in discovery of new cancer-related pathways with ontological annotations.

Visualization maps for the evolution of research hotspots in the field of regional health information networks

OBJECTIVES

The aim of this study was to reveal research hotspots in the field of regional health information networks (RHINs) and use visualization techniques to explore their evolution over time and differences between countries.

METHODS

We conducted a literature review for a 50-year period and compared the prevalence of certain index terms during the periods 1963-1993 and 1994-2014 and in six countries. We applied keyword frequency analysis, keyword co-occurrence analysis, multidimensional scaling analysis, and network visualization technology.

RESULTS

The total number of keywords was found to increase with time. From 1994 to 2014, the research priorities shifted from hospital planning to community health planning. The number of keywords reflecting information-based research increased. The density of the knowledge network increased significantly, and partial keywords condensed into knowledge groups. All six countries focus on keywords including Information Systems; Telemedicine; Information Service; Medical Records Systems, Computerized; Internet; etc.; however, the level of development and some research priorities are different.

CONCLUSIONS

RHIN research has generally increased in popularity over the past 50 years. The research hotspots are evolving and are at different levels of development in different countries. Knowledge network mapping and perceptual maps provide useful information for scholars, managers, and policy-makers.

The evolution of publication hotspots in the field of telemedicine from 1962 to 2015 and differences among six countries

Introduction Telemedicine has been implemented in many countries and has captured the attention of many researchers. Herein, we aim to quantify publication hotspots in the field of telemedicine, analyse their evolution, compare them in different countries, and provide visual representations. Methods We used software tools to process PubMed entries for a 54-year period and identified publication hotspots using keyword frequency analysis. We employed a keyword co-occurrence analysis, principal component analysis, multidimensional scaling analysis, and network visualization technology. Results The number of Medical Subject Heading (MeSH) terms increased with time. The most common subcategories of telemedicine between 1962 and 2015 were Remote Consultation, Teleradiology, and Telepathology. The most popular information communication technologies in telemedicine publications were related to the Internet and cell phones. The topics of Patient Satisfaction, Treatment Outcomes, and Home Care Services associated with telemedicine were highlighted after the 1990s. Use frequency of the terms Cell Phones and Self-Care increased drastically in the past six years, and the publication focus in six countries that had the highest output was different. Knowledge network maps and perceptual maps show the relationship between high-frequency MeSH terms. Discussion The telemedicine field has experienced significant growth and expansion in knowledge and innovation in the last 54 years. Publication hotspots for telemedicine lean towards clinical treatment, home care services, and personal care, and countries emphasize publishing in areas related to their national characteristics. This study quantitatively discusses publication hotspots, provides an objective and systematic understanding of this field, and suggests directions for future telemedicine research.

How can we establish more successful knowledge networks in developing countries? Lessons learnt from knowledge networks in Iran

BACKGROUND

Formal knowledge networks are considered among the solutions for strengthening knowledge translation and one of the elements of innovative systems in developing and developed countries. In the year 2000, knowledge networks were established in Iran's health system to organize, lead, empower, and coordinate efforts made by health-related research centers in the country. Since the assessment of a knowledge network is one of the main requirements for its success, the current study was designed in two qualitative and quantitative sections to identify the strengths and weaknesses of the established knowledge networks and to assess their efficiency.

METHODS

In the qualitative section, semi-structured, in-depth interviews were held with network directors and secretaries. The interviews were analyzed through the framework approach. To analyze effectiveness, social network analysis approach was used. That is, by considering the networks' research council members as 'nodes', and the numbers of their joint articles--before and after the network establishments--as 'relations or ties', indices of density, clique, and centrality were calculated for each network. In the qualitative section, non-transparency of management, lack of goals, administrative problems were among the most prevalent issues observed.

RESULTS

Currently, the most important challenges are the policies related to them and their management. In the quantitative section, we observed that density and clique indices had risen for some networks; however, the centrality index for the same networks was not as high. Consequently the attribution of density and clique indices to these networks was not possible.

CONCLUSION

Therefore, consolidating and revising policies relevant to the networks and preparing a guide for establishing managing networks could prove helpful. To develop knowledge and technology in a country, networks need to solve the problems they face in management and governance. That is, the first step towards the realization of true knowledge networks in health system.

P4 medicine: how systems medicine will transform the healthcare sector and society

Ten years ago, the proposition that healthcare is evolving from reactive disease care to care that is predictive, preventive, personalized and participatory was regarded as highly speculative. Today, the core elements of that vision are widely accepted and have been articulated in a series of recent reports by the US Institute of Medicine. Systems approaches to biology and medicine are now beginning to provide patients, consumers and physicians with personalized information about each individual's unique health experience of both health and disease at the molecular, cellular and organ levels. This information will make disease care radically more cost effective by personalizing care to each person's unique biology and by treating the causes rather than the symptoms of disease. It will also provide the basis for concrete action by consumers to improve their health as they observe the impact of lifestyle decisions. Working together in digitally powered familial and affinity networks, consumers will be able to reduce the incidence of the complex chronic diseases that currently account for 75% of disease-care costs in the USA.