Universal trajectories of scientific success

A Bibliometric Analysis of Research on Bacterial Persisters

BACKGROUND

In the past two decades, the surge of research on bacterial persisters has been inspired as increasingly concerning about the frequent failure of antibiotics treatment. This study was aimed at presenting a bibliometric and visualized analysis of relative publications on bacterial persisters, which offered insights into the development and research trends of this field.

METHODS

The Web of Science Core Collection and Ovid MEDLINE databases were utilized to retrieve relevant publications on bacterial persisters from 2001 to 2021. After manual selection, data including titles, authors, journals, author keywords, addresses, the number of citations, and publication years were subsequently extracted. The data analysis and visual mapping were conducted with Excel, SPSS, R studio, and VOSviewer.

RESULTS

In this study, 1,903 relevant publications on bacterial persisters were included. During 2001-2021, there was an exponential growth in the quantity of publications. It was found that these studies were conducted by 7,182 authors from 74 different countries. The USA led the scientific production with the highest total number of publications (859) and citation frequency (52,022). The Antimicrobial Agents and Chemotherapy was the most influential journal with 113 relevant publications. The cooccurrence analysis revealed that studies on bacterial persisters focused on four aspects: "the role of persisters in biofilms," "clinical persistent infection," "anti-persister treatment," and "mechanism of persister formation."

CONCLUSION

In the past two decades, the global field of bacterial persisters has significantly increased. The USA was the leading country in this field. Mechanistic studies continued to be the future hotspots, which may be helpful to adopt new strategies against persisters and solve the problem of chronic infection in the clinic.

Trends and hot topics in nurse empowerment research: A bibliometric analysis

AIM

The characteristics of the publications on nursing empowerment should be investigated in order to get an idea about the development of the field and to determine the developments in nursing practices and research. This study aimed to investigate the characteristics of the current knowledge structure and developmental process in nurse empowerment by using bibliometric methods.

METHODS

The data of this retrospective descriptive study were obtained from the Web of Science (WoS). All relevant studies conducted before 2020 were included in the study. The data analysis was conducted with Excel, SPSS and VOSviewer.

RESULTS

Five hundred eighty-nine studies conducted between 1974-2019 were included in the study. It was found that 72.15% of these studies were journal articles and these studies were conducted by 1,450 authors. The studies were published in 109 journals and were produced by researchers from 49 different countries. Also 47.5% of these studies were conducted in the United States. Hot topics were "empowerment", "nursing," "structural empowerment," "nurses," "job satisfaction" and "psychological empowerment."

CONCLUSION

There has been a noteworthy increase in the number of studies on nurse empowerment and this field offers an active field of study for nursing researchers. The focus of the studies varies from nurses to nurse managers.

Modeling Citation Trajectories of Scientific Papers

Several network growth models have been proposed in the literature that attempt to incorporate properties of citation networks. Generally, these models aim at retaining the degree distribution observed in real-world networks. In this work, we explore whether existing network growth models can realize the diversity in citation growth exhibited by individual papers – a new node-centric property observed recently in citation networks across multiple domains of research. We theoretically and empirically show that the network growth models which are solely based on degree and/or intrinsic fitness cannot realize certain temporal growth behaviors that are observed in real-world citation networks. To this end, we propose two new growth models that localize the influence of papers through an appropriate attachment mechanism. Experimental results on the real-world citation networks of Computer Science and Physics domains show that our proposed models can better explain the temporal behavior of citation networks than existing models.