ArticleFrontiers in plant science2023
Bibliometric analysis of real-time PCR-based pathogen detection in plant protection research: a comprehensive study.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The discovery of RT-PCR-based pathogen detection and gene expression analysis has had a transformative impact on the field of plant protection. This study aims to analyze the global research conducted between 2001 and 2021, focusing on the utilization of RT-PCR techniques for diagnostic assays and gene expression level studies. By retrieving data from the 'Dimensions' database and employing bibliometric visualization software, this analysis provides insights into the major publishing journals, institutions involved, leading journals, influential authors, most cited articles, and common keywords. Methods: The 'Dimensions' database was utilized to retrieve relevant literature on RT-PCR-based pathogen detection. Fourteen distinct search queries were employed, and the resulting dataset was analyzed for trends in scholarly publications over time. The bibliometric visualization software facilitated the identification of major publishing journals, institutions, leading journals, influential authors, most cited articles, and common keywords. The study's search query was based on the conjunction 'AND', ensuring a comprehensive analysis of the literature. Results: The analysis revealed a significant increase in the number of scholarly publications on RT-PCR-based pathogen detection over the years, indicating a growing interest and investment in research within the field. This finding emphasizes the importance of ongoing investigation and development, highlighting the potential for further advancements in knowledge and understanding. In terms of publishing journals, Plos One emerged as the leading journal, closely followed by BMC Genomics and Phytopathology. Among the highly cited journals were the European Journal of Plant Pathology, BMC Genomics, and Fungal Genetics and Biology. The publications with the highest number of citations and publications were associated with the United Nations and China. Furthermore, a network visualization map of co-authorship analysis provided intriguing insights into the collaborative nature of the research. Out of 2,636 authors analyzed, 50 surpassed the level threshold, suggesting active collaboration among researchers in the field. Discussion: Overall, this bibliometric analysis demonstrates that the research on RT-PCR-based pathogen detection is thriving. However, there is a need for further strengthening using modern diagnostic tools and promoting collaboration among well-equipped laboratories. The findings underscore the significance of RT-PCR-based pathogen detection in plant protection and highlight the potential for continued advancements in this field. Continued research and collaboration are vital for enhancing knowledge, developing innovative diagnostic tools, and effectively protecting plants from pathogens.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.