The Application of MicroRNAs in Glaucoma Research: A Bibliometric and Visualized Analysis

Glaucoma is similar to a neurodegenerative disorder and leads to global irreversible loss of vision. Despite extensive research, the pathophysiological mechanisms of glaucoma remain unclear, and no complete cure has yet been identified for glaucoma. Recent studies have shown that microRNAs can serve as diagnostic biomarkers or therapeutic targets for glaucoma; however, there are few bibliometric studies that focus on using microRNAs in glaucoma research. Here, we have adopted a bibliometric analysis in the field of microRNAs in glaucoma research to manifest the current tendencies and research hotspots and to present a visual map of the past and emerging tendencies in this field. In this study, we retrieved publications in the Web of Science database that centered on this field between 2007 and 2022. Next, we used VOSviewer, CiteSpace, Scimago Graphica, and Microsoft Excel to present visual representations of a co-occurrence analysis, co-citation analysis, tendencies, hotspots, and the contributions of authors, institutions, journals, and countries/regions. The United States was the main contributor. Investigative Ophthalmology and Visual Science has published the most articles in this field. Over the past 15 years, there has been exponential growth in the number of publications and citations in this field across various countries, organizations, and authors. Thus, this study illustrates the current trends, hotspots, and emerging frontiers and provides new insight and guidance for searching for new diagnostic biomarkers and clinical trials for glaucoma in the future. Furthermore, international collaborations can also be used to broaden and deepen the field of microRNAs in glaucoma research.

A Bibliometric Study on Trends in Proton Exchange Membrane Fuel Cell Research during 1990-2022

Proton exchange membrane fuel cell (PEMFC) with high density and safe reliability has been extensively studied in the world. With the circumstance of extensive PEMFC research, in this study we carried out a bibliometric analysis to understand the technological development. The information of 17,769 related publications from 1990 to 2022 was retrieved from the Web of Science Core Collection for bibliometric analysis based on the VOSviewer tool. The results show that the International Journal of Hydrogen Energy dominates among all of the source journals. The closest collaboration is between China and the USA, and publications from both of those account for 53.9% of the total. In terms of institutions, the Chinese Academy of Sciences has prolific publications, in which representative groups, such as Shao Zhigang's, have achieved many outputs in this field. The theme of PEMFC research can be divided into three aspects: "materials", "design" and "mechanisms". This study demonstrated overall mapping knowledge domain and systematic analysis, and contributed to making a guide for researchers on the progress and trends of PEMFC.

Examining association between cohesion and diversity in collaboration networks of pharmaceutical clinical trials with drug approvals

OBJECTIVE

Clinical trials ensure that pharmaceutical treatments are safe, efficacious, and effective for public consumption, but are extremely complex, taking up to 10 years and $2.6 billion to complete. One main source of complexity arises from the collaboration between actors, and network science methodologies can be leveraged to explore that complexity. We aim to characterize collaborations between actors in the clinical trials context and investigate trends of successful actors.

MATERIALS AND METHODS

We constructed a temporal network of clinical trial collaborations between large and small-size pharmaceutical companies, academic institutions, nonprofit organizations, hospital systems, and government agencies from public and proprietary data and introduced metrics to quantify actors' collaboration network structure, organizational behavior, and partnership characteristics. A multivariable regression analysis was conducted to determine the metrics' relationship with success.

RESULTS

We found a positive correlation between the number of successful approved trials and interdisciplinary collaborations measured by a collaboration diversity metric (P < .01). Our results also showed a negative effect of the local clustering coefficient (P < .01) on the success of clinical trials. Large pharmaceutical companies have the lowest local clustering coefficient and more diversity in partnerships across biomedical specializations.

CONCLUSIONS

Large pharmaceutical companies are more likely to collaborate with a wider range of actors from other specialties, especially smaller industry actors who are newcomers in clinical research, resulting in exclusive access to smaller actors. Future investigations are needed to show how concentrations of influence and resources might result in diminished gains in treatment development.

A Mixture Model of Truncated Zeta Distributions with Applications to Scientific Collaboration Networks

The degree distribution has attracted considerable attention from network scientists in the last few decades to have knowledge of the topological structure of networks. It is widely acknowledged that many real networks have power-law degree distributions. However, the deviation from such a behavior often appears when the range of degrees is small. Even worse, the conventional employment of the continuous power-law distribution usually causes an inaccurate inference as the degree should be discrete-valued. To remedy these obstacles, we propose a finite mixture model of truncated zeta distributions for a broad range of degrees that disobeys a power-law behavior in the range of small degrees while maintaining the scale-free behavior. The maximum likelihood algorithm alongside the model selection method is presented to estimate model parameters and the number of mixture components. The validity of the suggested algorithm is evidenced by Monte Carlo simulations. We apply our method to five disciplines of scientific collaboration networks with remarkable interpretations. The proposed model outperforms the other alternatives in terms of the goodness-of-fit.

The Impact of Collaboration Network on Water Resource Governance Performance: Evidence from China's Yangtze River Delta Region

Existing studies rarely examine the relationship between network structure and network performance. To fill this research gap, this article collects inter-local collaboration network data from 41 cities in the Yangtze River Delta region of China from 2009 to 2015. Based on the institutional collective action framework and social capital theory, we propose bridging and bonding hypotheses regarding the impact of network structures on governance performance. We employ social network analysis and panel data regression models to test the hypotheses. The results show that the coefficients for closeness centrality and clustering coefficient are statistically significant in this analysis, Wuxi played a central role in the collaboration network and the region had formed a close partner network, confirming the positive effect of bridging and bonding network social capital structures on network performance.

Link Definition Ameliorating Community Detection in Collaboration Networks

Collaboration networks are defined as a set of individuals who come together and collaborate on particular tasks such as publishing a paper. The analysis of such networks permits to extract knowledge on the structure and patterns of communities. The link definition and network extraction have a high impact on the analysis of collaboration networks. Previous studies model the connectivity in a network considering it as a binomial problem with respect to the existence of a collaboration between individuals. However, such a data consists of a high diversity of features that describe the quality of the interaction such as the contribution amount of each individual. In this paper, we have determined a solution to extract collaboration networks using corresponding features in a dataset. We define collaboration score to quantify the collaboration between collaborators. In order to validate our proposed method, we benefit from a scientific research institute dataset in which researchers are co–authors who are involved in the production of papers, prototypes, and intellectual properties (IP). We evaluated the generated networks, produced through different thresholds of collaboration score, by employing a set of network analysis metrics such as clustering coefficient, network density, and centrality measures. We investigated more the obtained networks using a community detection algorithm to further discuss the impact of our model on community detection. The outcome shows that the quality of resulted communities on the extracted collaboration networks can differ significantly based on the choice of the linkage threshold.

The Impacts of Transportation Infrastructure on Sustainable Development: Emerging Trends and Challenges

Transportation infrastructure has an enormous impact on sustainable development. To identify multiple impacts of transportation infrastructure and show emerging trends and challenges, this paper presents a scientometric review based on 2543 published articles from 2000 to 2017 through co-author, co-occurring and co-citation analysis. In addition, the hierarchy of key concepts was analyzed to show emerging research objects, methods and levels according to the clustering information, which includes title, keyword and abstract. The results expressed by visual graphs compared high-impact authors, collaborative relationships among institutions in developed and developing countries. In addition, representative research issues related to the economy, society and environment were identified such as cost overrun, spatial economy, prioritizing structure, local development and land value. Additionally, two future directions, integrated research of various effects and structure analysis of transportation network, are recommended. The findings of this study provide researchers and practitioners with an in-depth understanding of transportation infrastructure’s impacts on sustainable development by visual expression.

Evolving patterns in a collaboration network of global R&D on monoclonal antibodies

We investigated the evolution process of collaborative inter-organizational network of the research and development (R&D) on monoclonal antibody (mAb) over the past 30 y. The annual detection of the collaboration network provides dynamics on network structures and relationship changes among different organizations. Our research showed continuous growth of the network's scale and complexity due to the constant entry of new organizations and the forging of new partnering relationships. The evolving topological features reveal a core-periphery structure that became clearer over time and an increasing heterogeneity within the collaborative mAb R&D network. As measured by the number of network participants, dedicated biotechnology firms (DBFs) were the dominant organization form in the field of mAb development, but their average centrality was reduced during the period of 2004-2009, when pharmaceutical companies took over the positions of DBFs. Along with the network evolution, 2 waves of substitution on the leading positions were driven by technological innovations and mergers and acquisitions (M&A). In addition, this study analyzed organizational-level behaviors to help understand the evolution of network structures over the field of mAb development across the different technologically innovative or economic contexts.

Analyzing collaboration networks and developmental patterns of nano-enabled drug delivery (NEDD) for brain cancer

The rapid development of new and emerging science & technologies (NESTs) brings unprecedented challenges, but also opportunities. In this paper, we use bibliometric and social network analyses, at country, institution, and individual levels, to explore the patterns of scientific networking for a key nano area - nano-enabled drug delivery (NEDD). NEDD has successfully been used clinically to modulate drug release and to target particular diseased tissues. The data for this research come from a global compilation of research publication information on NEDD directed at brain cancer. We derive a family of indicators that address multiple facets of research collaboration and knowledge transfer patterns. Results show that: (1) international cooperation is increasing, but networking characteristics change over time; (2) highly productive institutions also lead in influence, as measured by citation to their work, with American institutes leading; (3) research collaboration is dominated by local relationships, with interesting information available from authorship patterns that go well beyond journal impact factors. Results offer useful technical intelligence to help researchers identify potential collaborators and to help inform R&D management and science & innovation policy for such nanotechnologies.