Background: Exosomes are small vesicles produced by almost all cells in the body and found in all biofluids. Cancer cell-derived exosomes are known to have distinct, measurable signatures, applicable for early cancer diagnosis. Despite the present bibliometric studies on "Cancer detection" and "Nanoparticles", no single study exists to deal with "Exosome" bibliometric study. Methods: This bibliometric work investigated the publication trends of "Exosomes" nanoparticles and its application in cancer detection, for the literature from 2008 to July 2019. The data were collected from the Web of Science Core Collection. There were variant visual maps generated to show annual publication, most-relevant authors, sources, countries, topics and keywords. The network analysis of these studies was investigated to evaluate the research trends in the field of exosomes. In addition, the data were qualitatively analyzed according to 22 top-cited articles, illustrating the frequently used subjects and methods in exosomes research area. Results: The results showed that the documents in this field have improved the citation rate. The top-relevant papers are mostly published in Scientific Reports journal which has lost its popularity after 2017, while today, Analytical Chemistry is leading in publishing the most articles related to exosomes. The documents containing keywords of plasma, cells, cancer, biomarkers, and vesicles as keywords plus, are more likely to be published in PLoS One journal. The clustering of the keywords network showed that the keyword theme of "extracellular vesicles" has the highest centrality rate. In global research, USA is the most corresponding country, followed by China, Korea and Australia. Based on the qualitative analysis, the published documents with at least 50 citations have used exosome release, cargo, detection, purification and secretion, as their targets and applied cell culture or isolation as their methods. Conclusion:The bibliometric study on exosomes nanoparticles for cancer detection provides a clear vision of the future research direction and identifies the potential opportunities and challenges. This may lead new researchers to select the proper subfields in exosomerelated research fields.
Time-series classification is utilized in a variety of applications leading to the development of many data mining techniques for time-series analysis. Among the broad range of time-series classification algorithms, recent studies are considering the impact of deep learning methods on time-series classification tasks. The quantity of related publications requires a bibliometric study to explore most prominent keywords, countries, sources and research clusters. The paper conducts a bibliometric analysis on related publications in time-series classification, adopted from Scopus database between 2010 and 2019. Through keywords cooccurrence analysis, a visual network structure of top keywords in time-series classification research has been produced and deep learning has been introduced as the most common topic by additional inquiry of the bibliography. The paper continues by exploring the publication trends of recent deep learning approaches for time-series classification. The annual number of publications, the productive and collaborative countries, the growth rate of sources, the most occurred keywords and the research collaborations are revealed from the bibliometric analysis within the study period. The research field has been broken down into three main categories as different frameworks of deep neural networks, different applications in remote sensing and also in signal processing for time-series classification tasks. The qualitative analysis highlights the categories of top citation rate papers by describing them in details. INDEX TERMS Time-series classification, deep learning, remote sensing, signal processing, bibliometrics, research productivity.
This bibliometric study investigated the public trends in the fields of nanoparticles which is limited to drug delivery and magnetic nanoparticles’ literature published from 1980 to October 2017. The data were collected from the Web of Science Core Collections, and a network analysis of research outputs was carried out to analyse the research trends in the nanoparticles literature. Nanoparticles and its applications are progressing in recent years. The results show that documents in the field of nanoparticles in chemistry and material science have improved in citation rate, as the authors were researching in multidisciplinary zones. Top-cited documents are mainly focusing on drug delivery, magnetic nanoparticles and iron oxide nanoparticles which are also the top research keywords in all papers published. Top-cited papers are mostly published in Biomaterials journal which so far has published 12% of top-cited articles. Although research areas such as contrast agents, quantum dots, and nanocrystals are not considered as the top-ranked keywords in all documents, these keywords received noticeable citations. The trends of publications on drug delivery and magnetic nanoparticles give a general view on future research and identify potential opportunities and challenges.
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