Emerging trends and new developments in regenerative medicine: a scientometric update (2000 - 2014).

Review

Emerging trends and new developments in regenerative medicine: a scientometric update (2000 -- 2014)

1.

Introduction

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Methods

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Most active topics

Chaomei Chen†, Rachael Dubin & Meen Chul Kim

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Emerging trends and new

Drexel University, College of Computing and Informatics, Philadelphia, PA, USA

Expert Opin. Biol. Ther. Downloaded from informahealthcare.com by University of Newcastle on 09/28/14 For personal use only.

developments 5.

Expert opinion

Introduction: Our previous scientometric review of regenerative medicine provides a snapshot of the fast-growing field up to the end of 2011. The new review identifies emerging trends and new developments appearing in the literature of regenerative medicine based on relevant articles and reviews published between 2000 and the first month of 2014. Areas covered: Multiple datasets of publications relevant to regenerative medicine are constructed through topic search and citation expansion to ensure adequate coverage of the field. Networks of co-cited references representing the literature of regenerative medicine are constructed and visualized based on a combined dataset of 71,393 articles published between 2000 and 2014. Structural and temporal dynamics are identified in terms of most active topical areas and cited references. New developments are identified in terms of newly emerged clusters and research areas. Disciplinary-level patterns are visualized in dual-map overlays. Expert opinion: While research in induced pluripotent stem cells remains the most prominent area in the field of regenerative medicine, research related to clinical and therapeutic applications in regenerative medicine has experienced a considerable growth. In addition, clinical and therapeutic developments in regenerative medicine have demonstrated profound connections with the induced pluripotent stem cell research and stem cell research in general. A rapid adaptation of graphene-based nanomaterials in regenerative medicine is evident. Both basic research represented by stem cell research and application-oriented research typically found in tissue engineering are now increasingly integrated in the scientometric landscape of regenerative medicine. Tissue engineering is an interdisciplinary field in its own right. Advances in multiple disciplines such as stem cell research and graphene research have strengthened the connections between tissue engineering and regenerative medicine. Keywords: CiteSpace, graphene,regenerative medicine, review, scientometrics, tissue engineering Expert Opin. Biol. Ther. (2014) 14(9):1295-1317

1.

Introduction

The fast-growing interdisciplinary field of regenerative medicine offers significant potential to advance understanding of cellular processes and to enable personalized treatments for a wide range of conditions by inducing and encouraging the restoration of diseased or damaged tissue [1]. As the field has advanced, the cutting edge of these treatments has moved from the use of grafts to replace tissue to the introduction of growth factors to encourage the natural tissue healing process, and finally to

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Article highlights. . .

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The literature of regenerative medicine has increased considerably over the past 2 years. The previously overwhelming dominance of research on induced pluripotent stem cells (iPSCs) found at the end of 2011 has shifted to multiple prominent areas of research, notably iPSC-related research, tissue engineering of a new generation and clinical and therapeutic applications. A rapid adaptation of graphene-based nanomaterials is evident. A profound impact on many aspects of regenerative medicine is expected. Interplays between basic research and applicationoriented research are expected in the further development of the increasingly interdisciplinary field.

This box summarizes key points contained in the article.

the more recent use of materials such as stem cells, each of which has the potential to differentiate into a wide range of different types of cells [2]. Thus, stem cells provide an ideal medium for generating tissues and have roused a great deal of excitement in the field. Acquiring stem cells for use in research and treatment poses a challenge. Embryonic stem cells are pluripotent, having the ability to differentiate into any cell type, and are therefore tremendously valuable for regenerative medicine applications. However, due to moral concerns surrounding the destruction of embryos for stem cell use, this line of research faces ethical as well as legal barriers [3]. Adult stem cells also provide many opportunities, such as the generation of bone tissue from adipose-derived mesenchymal stem cells [4] or the use of epithelial or mesenchymal dental stem cells for regenerative dentistry [2]. However, adult stem cells are comparatively limited and can differentiate only into cell types derived from their same embryological germ layer [3]. Induced pluripotent stem cells (iPSCs) offer an alternative option; these types of stem cells are derived from differentiated, mature cells, but like embryonic stem cells they are pluripotent. The 2012 Nobel Prize in Physiology or Medicine was awarded to John B. Gurdon and Shinya Yamanaka for their finding that pluripotent stem cells can be produced by ‘reprogramming’ mature, specialized cells. Gurdon performed his early work in this area with cells taken from tadpoles [5]. In this foundational study, he took nuclei from tadpole intestinal epithelium cells and transplanted them into tadpole eggs and found that some of the eggs with donor nuclei developed into normal tadpoles (others reaching various stages of abnormal development). These results demonstrated that a cell which was already differentiated contained sufficient genetic information to produce all types of cells necessary to develop a normal organism, and alluded to the possibility of transforming mature cells into pluripotent stem cells. In Takahashi and Yamanaka’s study [6], or in Takahashi2006, as we will refer to in the scientometric study, transcription factors supporting the continued pluripotency of embryonic stem cells were 1296

isolated and introduced into mature mouse cells (specifically tail-tip fibroblasts). The resulting cells were identical to embryonic stem cells and, when injected into live mice, formed tumors which contained cells from all three embryonic germ layers. The development of a pluripotent embryonic stem cell into its differentiated adult state was long thought to be a one way trip; cells became ever more specialized and, with only few exceptions, lost their ability to self-renew. Although it was initially believed that these adult cells lacked the genetic information required to produce different types of cells, Gurdon’s study demonstrated that sufficient information was present by implanting nuclei taken from adult tadpoles into tadpole eggs [5]. Nuclear transfer continued to serve as a powerful method, for instance, enabling the cloning of Dolly the sheep [7] and of other animals. However, this method is fairly inefficient, with many embryos generated from transferred nuclei experiencing genetic defects of various levels of severity due to factors such as the condition of the donor cell [8]. Other techniques were developed to improve efficiency, such as the use of teratocarcinomas to produce embryonal carcinoma cells (ECCs), which served as an immortal line of pluripotent cells. Although these ECCs are not typically effective in generating tissue directly, they can be used in cellular fusion to enable other cells to exhibit pluripotency [9]. However, it was the Nobel Prize-winning discovery that brought recognition of how the specific transcription factors at work in embryonic stem cells could induce pluripotency in a mature cell [6]. Our previous scientometric study of regenerative medicine presented a snapshot of the literature of regenerative medicine as of the end of November 2011 [10]. Since its data cutoff date was in 2011, we will refer to it as the 2011 review. In 2011, it became clear that two articles on the discovery of iPSCs had generated a tremendously high impact; they were highly cited at an increasingly faster rate -- a phenomenon known as a citation burst. They influenced many newer articles with the discovered technique. The significance of the iPSC research was detected in the literature several months prior to the announcement of the award of the Nobel Prize in October 2012. Responses to the breakthrough creation of iPSCs quickly focused on possible applications of the new technique; for example, the use of reprogrammed iPSCs to treat sickle-cell anemia in a mouse model [11] or the generation of new, healthy motor neurons from iPSCs derived from a patient with amyotrophic lateral sclerosis [12]. In addition to treatment applications, iPSCs have also been used to advance understanding of a variety of disorders including Alzheimer’s disease [13] and congenital long QT syndrome [14]. Researchers have investigated some of the drawbacks and dangers of iPSC generation and use. Suppression of the p53 pathway has been investigated as a means of improving the efficiency of iPSC generation and reducing the likelihood of malignant tumors [15]. Although the use of patient-derived iPSCs suggests the unlikelihood of rejection, immunogenicity

Expert Opin. Biol. Ther. (2014) 14(9)


Emerging trends and new developments in regenerative medicine

Figure 1. A lightweight survey of major topics on the Internet on regenerative medicine is shown. The visualizations were generated by the Carrot system based on first 100 results of search on regenerative medicine. Left: search results from the web. Right: search results from the PubMed.

of these cells has also been a topic of exploration [16]. Finally, mutations in iPSCs have been studied in mouse [9] and in human cells [17]. Alternative techniques for generation and use of iPSCs were also brought to discussion. Potentially risky viral integration of transcription factors were replaced with the use of neural progenitor cells and small molecules [18] or ‘piggyBac’ transposition [19]. Blood cells, including cord and peripheral blood, were used to generate iPSCs [20]. Recent work has also explored alternative ways of transforming mature, differentiated cells into pluripotent ones without the use of transcription factors. Mature cells were shown to become pluripotent once exposed to a low pH treatment, described as a ‘stimulus-triggered acquisition of pluripotency’ (STAP) [21], although the reproducibility of the work has been subsequently questioned. Another offshoot of research investigates reprogramming mature cells not into iPSCs but instead into other types of mature cells, such as cardiomyocytes [22], or neurons [23]. The literature relevant to regenerative medicine has grown rapidly, as we will show shortly. Two full years have elapsed since we conducted our scientometric study of the field at the end of 2011. In order to identify significant changes in the intellectual landscape of regenerative medicine, we revisit the literature of the field now to conduct a follow-up scientometric study of the field. The focus of the new scientometric

study is on the new developments that can be revealed using scientometric and visual analytic tools. According to information available on the Internet, major topics in regenerative medicine are shown in two visualizations in Figure 1. The two visualizations, known as form trees, were generated using Carrot, http://search.carrotsearch.com/ carrot2-webapp, based on the first 100 results of a web search (left) and the 100 results of a PubMed search (right). Stem cell-related research, tissue engineering, organs, repair, patients and would healing are among the leading topics in regenerative medicine. In this review, we will rely on scholarly publications in the Web of Science as a more rigorous and reliable representation of the literature. The lightweight view generated using Carrot provides a useful point of reference so that any substantial discrepancies would be accounted for. 2.

Methods

The new study will utilize several scientometric and visual analytic methods. In particular, burst detection will be applied to subject categories and keywords assigned to publications in a citation-expanded collection of articles relevant to regenerative medicine as well as noun phrases extracted from titles and abstracts of these articles. In addition, burst detection will be applied to articles in terms of the growth rate of their


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Table 1. A summary of the datasets collected. Dataset


DA DB DC DD DABCD

Collection Topic search Citation expanded Topic search Citation expanded Combined

Duration

Results

Articles

Reviews

References

Keywords

Authors

Institutions

------

4331 67,421 2626 5069 71,393

2853 52,490 1901 3805 61,049

1235 13,056 670 1208 16,169

267,545 4,243,303 169,097 361,782 5,041,714

62,028 965,899 38,127 72,473 1,138,527

21,594 389,534 14,969 29,377 455,474

11,712 199,613 7963 15,690 234,978

2000 2000 2012 2012 2000

2011 2014 2014 2014 2014

citations. A burst of an event is a surge of the frequency of the event, such as the appearance of a keyword or the citation of an article. The structure and dynamics of the literature of regenerative medicine will be analyzed in terms of progressively synthesized networks derived from citations made by citing articles that meet various selection criteria. Synthesized networks will be decomposed into clusters as tightly coupled references to represent a common theme of research. Next, these clusters will be labeled by using terms extracted from the titles of the most representative citing articles for each cluster. Several different forms of visualizations will be generated to highlight new developments since 2012. Although many of the techniques were used in the preparation of our 2011 review, a few new techniques will be used in the new study. Different networks of articles can be generated by defining the similarity between two articles differently. For example, similarities defined in terms of co-citation strengths would lead to co-citation networks, whereas similarities based on bibliographic coupling would lead to a different network. Dual-map overlays introduced in [24] provide a global visualization of the growth of the literature at a disciplinary level. The dual-map visualization will be used in this study with recently published high-impact articles as overlays. A predictive modeling method introduced in [25] estimates the transformative potential of an article in terms of structural variation metrics. This method will be used in the new study to identify a list of potentially significant articles based on their structural novelty. Although there are an increasing number of science mapping systems and generic tools [26], few systems are readily accessible and specifically designed to meet the needs for generating a systematic review of a fast-moving and complex field, especially with features to facilitate the detection and interpretation of emerging trends and transition patterns for analysts who are not domain experts. CiteSpace is particularly designed to support the complete analytic process of visualizing and analyzing scientific literature. It has been used for performing several hundreds of scientometric studies. An earlier version of CiteSpace was used in our 2011 review. Datasets Our new review of regenerative medicine aims to provide an update of our 2011 review. Our 2011 review started with a topic search for ‘regenerative medicine’ between 2000 and 2.1

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2011 and focused on original research articles and review papers only with a 3875-record core dataset and an expanded dataset of 35,963 records through forward-citation links. The expanded dataset, consisting of 28,252 articles of original research and 7711 review articles, was considered to be an adequate representation of the regenerative medicine literature. We made the decision to include original research papers and review papers primarily for two reasons: i) original research papers are representative of the state of the art of the field, although other types of documents such as letters may also represent the state of the art; and ii) review papers represent an additional layer of representative papers selected by domain experts (i.e., the authors of review papers). The new review aims to identify new developments in the field of regenerative medicine since our 2011 review. We first reconstructed the core dataset used in the 2011 review as the dataset DA, then expanded the core dataset with forwardcitation links to articles published between 2000 and 2014 as the dataset DB. Next, we used the same topic search to retrieve new articles published after the cutoff date of our 2011 review data collection as the dataset DC. Similarly, DC was expanded through forward citations to form a new expanded dataset DD. The purpose is to identify how many new articles appeared since our previous review (Table 1). Finally, all the four individual datasets were combined. After duplicates were removed, the final dataset DABCD was used for the analysis. We used the dataset DABCD as our primary source but also used other individual datasets in the review. The growth of the literature over the past 2 years is enormous. The 4331-article core in 2011 has grown to 6957 in February 2014, a 60.6% of increase. The 35,963-record expanded dataset in 2011 is almost doubled to 71,393 in February 2014, a 98.5% of increase. Table 1 summarizes these datasets. Figure 2 visualizes a broad context of research in regenerative medicine in terms of research fronts and the intellectual base. Current research fronts are built on an underlying intellectual base through backward citations [27]. The intellectual base consists of 34,805 references cited by research front articles drawn from the dataset DABCD (2000 -- 2014). Research front articles are selected if they are among the 5000 most-cited articles published in the same year during the 15-year time span. Research front articles are shown as dots in red, whereas intellectual-base references are shown as dots in other colors that indicate the year of their publication.




Figure 2. The research fronts and the intellectual base of regenerative medicine are shown. Red dots in the foreground represent research front articles of the DABCD dataset (2000 -- 2014). A total of 34,805 dots of various other colors in the background represent references that form the intellectual base, which are linked to research front articles by forward citations. The underlying intellectual base network is derived from citations made by up to 5000 most-cited research front articles per year during the 15-year period between 2000 and 2014. The first authors of the highest cited references are labeled, notably Takahashi as the first author of the two groundbreaking induced pluripotent stem cells articles.

For example, references published in early years of the time span are colored in blue, light blue and green, whereas more recently published references are colored in yellow and orange. The position of iPSC research is approximately centered in the rectangular area in the universe of regenerative medicine. The name of the first author of Takahashi2006 and Takahashi2007 is marked by labels. In the rest of this article, we will use an abbreviated notion to refer to articles identified in the review with the name of its first author only and the year of its publication, for example, as in Takahashi 2006; this is in part because the cited reference field of a bibliographic record in the Web of Science does not include coauthors’ names. CiteSpace CiteSpace visualizes the literature in the form of a co-citation network, which draws on article citations to reveal the structure of a field or fields [27-29]. CiteSpace was used in our 2011 review and will be used for this new review as well. References cited by a given article provide valuable information regarding intellectual connections between various 2.2

scientific concepts [25,30]. In a co-citation network, an edge is created between two article nodes when a third article cites them together, and conceptual clusters are formed as certain groups of articles are repeatedly referenced in conjunction with one another. CiteSpace depicts the changes which occur in a body of literature over time by overlaying ‘time slice’ networks, each including the citations made in 1 year. The new works published each year may strengthen existing relations between articles or make new ones, and by comparing these time slices, we reveal the ever-changing recognition of meaningful concepts made by researchers in a field. CiteSpace uses color-coded nodes and edges to discriminate between combined networks, with each year in a dataset assigned to its own color. The color of a network edge indicates the year in which the co-citation link was first made. Nodes are made up of ‘tree rings’ of different colors, the thickness of which represents the number of citations the article received in a particular year. A red ring present in a particular year denotes a citation burst, that is, a surge of citations in that year. A purple ring is used to represent the degree of a node’s betweenness centrality. A node with high betweenness centrality links a conceptual cluster in one time with one in another


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and can be seen as a bridge extending from earlier to more recent ideas. Document co-citation analysis A document co-citation network represents a network of references that have been co-cited by a set of publications. CiteSpace first generates a document co-citation network from articles published in a single year y as a Document co-citation analysis (DCA[y]), then it integrates a time series of the individual DCAs for all the years in a time interval and produces a synthesized network. The dataset DABCD represents an expanded literature of regenerative medicine because it contains not only publications that are matched by topic fields, namely, titles, abstracts, and keywords, but also publications that cite topic-matched articles. Specifically, the dataset DABCD contains 71,393 articles published in the 15-year period between 2000 and 2014 The date of data retrieval was 2 February 2014. When generating an individual DCA network, a selection threshold is set so that whether an article’s citation behavior will be taken into account will dependent on the extent the article has been cited in its own right. For example, we may set the threshold to be top 100 highly cited articles to represent the citation patterns of a particular year. In other words, articles that failed to be ranked high enough will not be able to contribute to the network structure, which is preferable because we would be more interested in the citation pattern of recognized works. In this article, we typically use top 100 articles per year as the selection. CiteSpace provides functions to reduce the number of links while retaining the most salient structure. The link reduction functions include a Minimum Spanning Tree pruning and a Pathfinder Network Scaling pruning. Burst detection identifies articles that have attracted the attention of peer scientists. CiteSpace supports the detection of two types of burst: citation-based burst detection and occurrence-based burst detection.


2.2.1

Dual-map overlays Dual-map overlays introduced in [24] are used for this study. Dual-map overlays are superimposed on a global basemap of scientific literature. The global basemap consists of two component maps, hence the term dual-map overlays. One component basemap represents a network of over 10,000 journals in terms of their similarity as citing journals computed based on the 2011 Journal Citation Report of Thomson Reuters. The similarity between two citing journals is determined by the frequency distribution of how often they cite other journals. The other component also represents a network of over 10,000 journals but in terms of their similarities as cited journals. A distinct set of publications can be used to generate a dual-map overlay. For each article in the publication set, the journal in which it appears is located in the network of citing journals on the left-hand side of the basemap. References cited 2.2.2

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by the articles are located in the network of cited journals on the right-hand side of the basemap. The trajectories of the set over time are shown in terms of the paths formed by the weight centers of corresponding publications and references, retrospectively. The dynamics of the underlying research activities can be revealed in terms of the stability of the corresponding trajectories. The dataset DA contains 3875 articles published between 2000 and 2011. They were retrieved by a topic search for ‘regenerative medicine’ in the Web of Science. The dataset DA was used in our 2011 review of the field [10], which is used as a point of reference for the new review. A dual-map overlay of the dataset is generated to illustrate the disciplines in which these articles were published and the disciplines from which these articles refer to through citation links as the source of their inspirations. 3.

Most active topics

Regenerative medicine is an interdisciplinary field of study, which not only involves numerous disciplinary areas but also demonstrates shifts of the intensity of publications in terms of abrupt changes of subject categories and keywords of these publications as well as their citations. Each publication indexed in the Web of Science is assigned one or more subject categories such as oncology, pathology and microbiology. Each publication is also assigned a number of keywords. The shift of these fast-increasing subject categories or keywords indicates the most active areas of publications at a disciplinary level. The burstness of subject categories, keywords or cited references is a valuable indicator of most active research topics at various levels of granularity. Subject categories (2000 -- 2014) Burst detection revealed subject categories that increased abruptly over time (Figure 3). Subject categories of articles in the combined expanded dataset DABCD (2000 -- 2014) were analyzed for their burstness. A total of 71,327 of the 71,393 records have valid subject categories. A total of 186 unique subject categories were found. Occurrence bursts were detected in association with 47 subject categories. This time interval is depicted as a blue line. The period time in which a subject category was found to have a burst is shown as a red line segment, indicating the beginning year and the ending year of the duration of the burst. For example, at the top of the list, subject category general & internal medicine has a period of burst between 2000 and 2005 with a burst strength of 7.3231. Hot areas prior to 2008 all belong to the disciplines of medicine and biology. Hot areas associated with social sciences appeared for the first time in 2008. social sciences -- other topics, ethics and social issues have strong bursts from 2008 to 2009 with burst strengths over 6.0348. Notably, both Nanoscience & nanotechnology (12.6639) and energy & fuels (8.6723) have very strong bursts since 2013. 3.1




Figure 3. Among 186 subject categories, 47 subject categories have occurrence bursts during (2000 -- 2014).

At a finer-grained level, burst patterns of keywords, that is, indexing terms, may also reveal what was new in regenerative medicine. Each year up to 5000 most-frequently appeared keywords were selected for the burst detection throughout the 15-year time span (2000 -- 2014), which resulted in 21,044 unique keywords in total. The most common keywords are in-vitro, appearing in 11,479 articles and differentiation, appearing in 10,689 articles. The keyword regenerative medicine appeared in 1728 articles. Among the top 100 keywords with the strongest strength of burst, we are particularly interested in those keywords that started to burst from 2009 onward (Figure 4). For example, the keyword human somatic cells bursted between 2009 and 2010 with a burst strength of 29.2. Keywords starting their bursts since 2013 include nanoparticles (22.1), which appeared in 1515 articles, neuroregeneration (18.9, in 65 articles) and graphene (16.0, in 59 articles). The number of articles with graphene listed as a keyword in the expanded dataset DABCD has increased rapidly from 3 in 2010 to 125 in 2013. For example, an article on ‘selfassembled graphene hydrogel via a one-step hydrothermal process’ [8] in the expanded dataset was cited 290 times. The most commonly appeared phrases in the titles of these articles are nanomaterials (16 times), carbon dots (7 times), carbon nanotubes (6 times), graphene hydrogels and graphene oxide (both 5 times). In the expanded dataset, three articles published in 2012 contain both graphene and regenerative either in the title or in the abstract. There are seven such articles in 2013. For example, one article is about ‘a graphene-based platform for iPSCs culture and differentiation’ [11] and another about ‘self-supporting graphene hydrogel film as an experimental

platform to evaluate the potential of graphene for bone regeneration’ [12]. These connections between graphene as nanomaterials and central concepts in regenerative medicine and tissue engineering such as iPSCs and hydrogels provide strong evidence that the surge of the keyword graphene indeed indicates a profound synergy between nanotechnology and regenerative medicine.

Cited references (2000 -- 2014) Specific growth areas in the field are characterized by articles that experienced citation bursts. Figure 5 shows top 100 references with strongest citation bursts during the period between 2000 and 2014. This was based on up to 5000 most-cited articles per year as the citing articles. A total of 34,805 references were selected from 9,262,246 valid references. There are another 3894 invalid references because of incomplete components. The article with the strongest citation burst is Takahashi2006, which was one of the two landmark papers that led to the 2012 Nobel Prize in medicine for research on iPSCs. Its burst lasted for 3 years from 2009 to 2011 with a burst strength of 427.6 (Table 2). The burst of this article was also detected in our previous review published in 2012. Takahashi2007 has the fourth-strongest burst of 226.2 from 2009 to 2012. Table 3 shows the references that have the most recent citation bursts from 2011 onward. Several articles published in 2010 started their citation bursts in 2011. So did one 2009 article and two 2011 articles. Citation bursts starting from 2012 are associated with four 2011 articles. Since their citation 3.2


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Figure 4. Keywords with periods of burst from 2009 onward based on up to 5000 most frequently appeared keywords per year for 15 years (2000 -- 2014) are shown.

Figure 5. A total of 100 references with the strongest citation bursts over the period between 2000 and 2014 are shown. The burst detection was based on the citations made by the top 5000 articles per year during the 15-year time span.

bursts occurred after the publication of our 2011 review, we will inspect these four 2011 articles in further detail. Among the articles with strong citation bursts since 2012, Hanahan and Weinberg’s 2011 article [31], titled ‘Hallmarks of cancer: The next generation’, has the strongest citation burst with a burst strength of 93.1. This article published in 1302

Cell identified six biological hallmarks of cancer and argued that reprogramming of energy metabolism and evading immune destruction as two emerging hallmarks being added to the list. The second article with the most recent citation burst studies the immunogenicity of iPSCs [32]. The work has clinical implications on any therapeutic use of autologous



Table 2. Top five references with the strongest citation bursts during 2000 -- 2014. Citation burst Rank


1 2 3 4 5

References Takahashi and Yamanaka (2006) CELL, V126, P663 [6] Reya et al. (2001) NATURE, V414, P105 [42] Jiang et al. (2002) NATURE, V418, P41 [43] Takahashi et al. (2007) CELL, V131, P861 [35] Jain (2005) SCIENCE, V307, P58 [44]

Strength

Begin

End

Duration (2000 -- 2014)

427.6 312.5 255.7 226.2 203.4

2009 2002 2002 2009 2006

2011 2006 2007 2012 2010

ၽၽၽၽၽၽၽၽၽၾၾၾၽၽၽ ၽၽၾၾၾၾၾၾၽၽၽၽၽၽၽ ၽၽၾၾၾၾၾၾၽၽၽၽၽၽၽ ၽၽၽၽၽၽၽၽၽၾၾၾၾၽၽ ၽၽၽၽၽၽၾၾၾၾၾၽၽၽၽ

Table 3. References with the most recent bursts from 2011. Citation burst Rank

References (#)

Strength

Begin

End

Duration (2000 -- 2014)

1

Warren et al. (2010) CELL STEM CELL, V7, P618 [45]

113.0

2011

2014

ၽၽၽၽၽၽၽၽၽၽၽၾၾၾၾ

2 3 4 5 6 7 8 9

Kim et al. (2010) NATURE, V467, P285 [32] Vierbuchen et al. (2010) NATURE, V463, P1035 [23] Ieda et al. (2010) CELL, V142, P375 [22] Lister et al. (2011) NATURE, V471, P68 [46] Gore et al. (2011) NATURE, V471, P63 [17] Slaughter et al. (2009) ADV MATER, V21, P3307 [47] Polo et al. (2010) NAT BIOTECHNOL, V28, P848 [48] Hanahan and Weinberg (2011) CELL, V144, P646 [31]

102.8 93.7 90.7 90.1 73.0 69.3 65.3 93.1

2011 2011 2011 2011 2011 2011 2011 2012

2014 2014 2014 2014 2014 2014 2014 2014

ၽၽၽၽၽၽၽၽၽၽၽၾၾၾၾ ၽၽၽၽၽၽၽၽၽၽၽၾၾၾၾ ၽၽၽၽၽၽၽၽၽၽၽၾၾၾၾ ၽၽၽၽၽၽၽၽၽၽၽၾၾၾၾ ၽၽၽၽၽၽၽၽၽၽၽၾၾၾၾ ၽၽၽၽၽၽၽၽၽၽၽၾၾၾၾ ၽၽၽၽၽၽၽၽၽၽၽၾၾၾၾ ၽၽၽၽၽၽၽၽၽၽၽၽၾၾၾ

10 11 12

Zhao et al. (2011) NATURE, V474, P212 [16] Anokye-Danso et al. (2011) CELL STEM CELL, V8, P376 [33] Dasha et al. (2011) PROG POLYM SCI, V36, P981 [34]

78.9 78.7 71.0

2012 2012 2012

2014 2014 2014

ၽၽၽၽၽၽၽၽၽၽၽၽၾၾၾ ၽၽၽၽၽၽၽၽၽၽၽၽၾၾၾ ၽၽၽၽၽၽၽၽၽၽၽၽၾၾၾ

cells derived from iPSCs. The third article that has drawn much attention since 2012 is a 2011 article by AnokyeDanso et al. [33] on a more efficient reprogramming approach than existing approaches to reprogram somatic cells to pluripotency. The fourth article is a review article by Dasha et al. [34] published in 2011 on new developments of chitosan-based biomedical applications, including the chemical and biological properties of chitosan for regenerative medicine. Table 4 list the full references of these four articles and sentences selected from their abstracts. Their numbers follow Table 3. Articles 9, 10 and 11 are related to various aspects of iPSC research. Articles 9 and 12 are particularly relevant to regenerative medicine. Figure 5 shows 100 references with the strongest citation bursts in the order of the beginning year of a period of citation burst. 4.

Emerging trends and new developments

Clusters of co-cited references (2000 -- 2014) The datasets DA and DC collectively represent the literature defined by a topic search for regenerative medicine 4.1

(2000 -- 2014). Our 2011 study reviewed the literature up to November 2011. What has changed since then? Figure 6 shows two versions of a snapshot of the literature as of the beginning of February 2014. The structure of the field is characterized by a synthesized network of 719 references co-cited between 2000 and 2014 by the top 100 most-cited articles per year. The version of the snapshot on the left depicts landmark articles as large citation rings and hotspot articles with citation bursts shown as citation rings in red. The colors of links in this version represent the first year when those links were made. The version on the right highlights new developments (colored in blue) after our 2011 review and shows how new clusters are connected with previously established areas (colored in red). For example, two new areas can be identified as cluster #8 on cell sheeting engineering (located near the top of the network) and cluster #4 on biologic scaffold (located in the northeast quadrant). The focus of the two newly emerged clusters of co-cited references, #4 and #8, can be partially revealed in terms of what the articles that cite the members of these clusters have in common. For example, Table 5 lists articles that cited > 10% of the members of cluster #4 and 22% of the members of cluster #8.


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Table 4. The four hot articles with citation bursts since 2012. First authors’ names are used in the references. Additional co-authors, if any, are not included. Rank 9

10


11

12

References with citation bursts since 2012 Hanahan and Weinberg (2011) [31] Hallmarks of Cancer: The Next Generation. CELL, V144, P646. ‘Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list -- reprogramming of energy metabolism and evading immune destruction’. Zhao et al. (2011) [16] Immunogenicity of induced pluripotent stem cells. NATURE, V474, P212. ‘These findings indicate that, in contrast to derivatives of ESCs, abnormal gene expression in some cells differentiated from iPSCs can induce T-cell-dependent immune response in syngeneic recipients. Therefore, the immunogenicity of therapeutically valuable cells derived from patient-specific iPSCs should be evaluated before any clinic application of these autologous cells into the patients’. Anokye-Danso et al. (2011) [33] Highly efficient miRNA-mediated reprogramming of Mouse and human somatic cells to pluripotency. CELL STEM CELL, V8, P376. ‘This miRNA-based reprogramming approach is two orders of magnitude more efficient than standard Oct4/Sox2/ Klf4/Myc-mediated methods’. Dash et al. (2011) [34] Chitosan -- A versatile semi-synthetic polymer in biomedical applications. PROG POLYM SCI, V36, P981. ‘Chitosan is a polyelectrolyte with reactive functional groups, gel-forming capability, high adsorption capacity and biodegradability. In addition, it is innately biocompatible and non-toxic to living tissues as well as having antibacterial, antifungal and antitumor activity. These features highlight the suitability and extensive applications that chitosan has in medicine. Micro/nanoparticles and hydrogels are widely used in the design of chitosan-based therapeutic systems. The chemical structure and relevant biological properties of chitosan for regenerative medicine have been summarized as well as the methods for the preparation of controlled drug release devices and their applications’.

ESCs: Embryonic stem cells; iPSCs: Induced pluripotent stem cells.

Figure 6. Illustrations of regenerative medicine (2000 -- 2014). Left: Landmark nodes (large citation tree rings) and hotspot articles (citation bursts in red rings). Right: New developments (colored in blue) since our 2011 review, for example, #8 cell sheet engineering and #4 biologic scaffold, versus previously identified areas (colored in red).

For cluster #4, unique title terms identified in this group of citing articles include the term “ control” in the context of controlled stem cell differentiation, control of live cells and control of neuronal cell adhesion. The pattern in cluster #8 is clearer than in the cluster #4. The term ‘cell sheet engineering’ consistently appeared in the titles of three of the four top-citing articles of cluster #8. Cluster #8 also has a higher degree of concentration than cluster #4. Each of the first two citing articles in cluster #8 cited over 35% of the cluster members (Table 5). In particular, 1304

the first article’s title provides a self-explained definition of what cell sheet engineering is about: a unique nanotechnology for scaffold-free tissue reconstruction with clinical applications in regenerative medicine. New co-citation clusters in 2012 -- 2014 To clearly identify new developments in regenerative medicine since our 2011 scientometric study, we generated two timeline visualizations based on the combined dataset DABCD, a citation-expanded representation of the literature of 4.2




Table 5. A list of articles that contributed to clusters #4 control or biologic scaffold and #8 cell sheet engineering. Cluster #

Rank

Coverage (%)

4 4

1 2

11 10

4 4 4 8

3 4 5 1

10 10 10 39

8 8

2 3

35 26

8

4

22

Representative articles

Ref.

Liu et al. (2010) Synthetic hydrogels for controlled stem cell differentiation Kim et al. (2010) Biomimetic nanopatterns as enabling tools for analysis and control of live cells Pattersona et al. (2010) Biomimetic materials in tissue engineering Roacha et al. (2010) Surface strategies for control of neuronal cell adhesion: a review Teo et al. (2010) Nanotopography/mechanical induction of stem-cell differentiation Elloumi-Hannachi et al. (2010) Cell sheet engineering: A unique nanotechnology for scaffold-free tissue reconstruction with clinical applications in regenerative medicine Kelm and Fusseneggar (2010) Scaffold-fee cell delivery for use in regenerative medicine Itogaa and Okanoa (2010) The high functionalization of temperature-responsive culture dishes for establishing advanced cell sheet engineering Egami et al. (2014) Latest status of the clinical and industrial applications of cell sheet engineering and regenerative engineering

[49] [50]

Figure 7. A timeline visualization for T2000

-- 2011

[51] [52] [53] [54] [55] [56] [57]

is shown.

Figure 8. A timeline visualization for T2000 -- 2014 is shown. New developments since 2012 are included in the visualization, notably in association with clusters #5, #8, #11 and #13. Expert Opin. Biol. Ther. (2014) 14(9)

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Figure 9. A network of 1552 co-cited references representing citation patterns of top 300 articles per year between 2006 and 2014.

regenerative medicine. Since the dataset DABCD contains articles published between 2000 and 2014, it is convenient to adjust the scope of timeline visualization for the period between 2000 and 2011 as T2000 -- 2011 and the entire period between 2000 and 2014 as T2000 -- 2014. Visualized clusters are determined based on citation instances made by the top 100 mostcited articles per year for T2000 -- 2011 and T2000 -- 2014. Timeline visualizations will make newly emerged threads of research 1306

standout so that they can be recognized more easily. Timeline visualizations for T2000 -- 2011 and T2000 -- 2014 are shown in Figures 7 and 8, respectively. First, the largest cluster in both timeline visualizations is cluster #0 -- iPSCs. The two large circles with red rings depict the references to Takahashi2006 [6] and Takahashi2007 [35]. The overall size of the circle increased for both references between the visualized datasets, indicating increased citations to these landmark publications.



Table 6. Largest clusters of co-cited references among the 294 clusters.


Cluster Size Silhouette Average ID year 0 1 2 3 4 5 6 7 8 9 10 11

101 77 77 77 62 58 55 51 50 49 46 45

0.939 0.918 0.876 0.886 0.805 0.897 0.974 0.938 0.901 0.918 0.945 0.978

2005 2005 2007 2008 2010 2009 2011 2004 2007 2005 2011 2006

12 13 14

41 40 39

0.968 0.87 0.872

2010 2010 2004

15

39

0.972

2011

17 16 18

37 37 33

0.993 0.933 0.961

2005 2011 2010

19

32

0.965

2011

Label by TF*IDF

Label by log-likelihood ratio

Precancerous stem cell Phosphoinositide Cardiac stem cell therapy Mesentery-derived cell b-cell Genome-wide identification Receptor-expressing T-cell LongSAGE profiling Bisulfite sequencing Atlantic salmon Active mechanical perfusion IL-6-dependent PGE2 secretion New kid Extended passaging Cell fusion

Cancer stem cell Embryonic stem cell Cardiomyocyte Induced pluripotent stem cell Induced pluripotent stem cell Pluripotency Design Human embryonic stem cell DNA methylation Mesenchymal stem cell Extracellular matrix Mesenchymal stem cell

Kidney transplantation tolerance Osteoblastic niche Human cardiosphere Leucine-rich repeat-containing G-protein-coupled receptor Mineralized collagen scaffold

Organ transplantation

Clusters representing new developments since our 2011 review are shown in Figure 8 as the co-citation activities appeared to the left of the column of the clusters’ labels. New clusters include #5 on hydrogel, #8 on cancer, #11 on cardiomyocyte and #13 on mesenchymal stromal cell. A more detailed visualization was generated to further investigate new developments in regenerative medicine. A network of 1552 co-cited references was generated by sampling the citation behavior of the top 300 articles per year from DABCD for the period between 2006 and 2014 and by applying Pathfinder Network Scaling to the 15 individual networks before they were synthesized by CiteSpace (Figure 9). The visualized network reveals the overall structure of regenerative medicine in a broader context because the citing articles were drawn from the expanded and integrated dataset DABCD. The overall structure consists of three major areas of activity: the lower half of the network with links essentially in blue, which correspond to co-citations made in the first 3 years of the period between 2006 and 2014; the central area of the network with links in green and yellow, which are co-citations mostly made in the second 3 years of the period between 2006 and 2014, that is, 2009 to 2011; and the upper half of the network with co-citation links mostly in red, which imply that these co-citations were made in the most recent 3 years, that is, between 2012 and 2014. Our previous review was based on the literature up to November 2011. The new developments since then are represented by the upper half of the network. The 1552 reference network is divided into 294 clusters. The earliest structure consists of some of the largest clusters

Label by mutual information

Niche Myocardial cell sheet therapy Intestinal stem cell

Breast Sox2 Developmental paradigm Multipotent isl1 Switching Mutant p53 facilitate Cancer gene therapy Embryoid body homogeneity Promoter DNA methylation Human skin fibroblast Infection Allogeneic mesenchymal stem cell therapy DNA methyltransferase Exploiting pluripotency Bone marrow-derived cell therapy Acellular cartilage matrix scaffold Defect Capturing epidermal stemness Aldehyde dehydrogenase

Nanofibrous mat

Cardiac myocyte

Active DNA demethylation Induced pluripotent stem cell Cell fusion

such as #0 cancer stem cell, #1 embryonic stem cell, #7 human embryonic stem cell and #9 mesenchymal stem cell. These research areas were clearly identified in our previous survey. The midterm structure primarily includes #2 cardiomyocyte, #3 and #4 iPSC and #5 pluripotency. The most recent structure consists of #6 design, #10 extracellular matrix, #15 organ transplantation and #16 myocardial cell sheet therapy and a few smaller clusters. At the level of co-citation clusters, it appears that research on iPSCs remains to be a central topic in the context of regenerative medicine. Further, newly emerged clusters have strong clinical implications and they are closely related to tissue engineering. The largest cluster #0 cancer stem cell has over 100 references as its members with an average year of publication of 2005. Its high silhouette value of 0.939 indicates a high homogeneity of the cluster (Table 6). The largest cluster formed by more recently published articles is #6, which is labeled as design and receptor-expressing T cell. The cluster has 55 members and an average year of publication of 2011. Table 7 shows five articles in cluster #6 with the strongest citation bursts. The term ‘receptor-modified T cells’ appeared in the titles of two of the five articles. Table 8 shows four most-representative articles of cluster #6. A common theme across the four representative citing articles is the design of an immunotherapy with receptor-modified T cells. Other clusters of the same age include cluster #10 extracellular matrix, #15 organ transplantation and #16 myocardial cell sheet therapy. Table 9 lists five articles in cluster #10


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Table 7. Articles with the strongest citation bursts in cluster #6. Citation

Burst

118

46.34

70

Author

Year

Title

Source

Ref.

Stasi et al.

2011

NEW ENGL J MED

[58]

26.78

Porter et al.

2011

NEW ENGL J MED

[59]

44

17.27

Tormin et al.

2011

BLOOD

[60]

40

15.7

Brentjens et al.

2011

BLOOD

[61]

34

13.93

Savoldo et al.

2011

Inducible apoptosis as a safety switch for adoptive cell therapy Chimeric antigen receptor-modified T cells in chronic lymphoid leukemia CD146 expression on primary nonhematopoietic bone marrow stem cells is correlated with in situ localization Safety and persistence of adoptively transferred autologous CD19-targeted T cells in patients with relapsed or chemotherapy refractory B-cell leukemias CD28 costimulation improves expansion and persistence of chimeric antigen receptor-modified T cells in lymphoma patients

J CLIN INVEST

[62]

Table 8. Articles that cite over 20% members of cluster #6. Coverage (%) 36 29 20 20

Articles citing cluster #6

Ref.

Ruella and Kalos (2014) Adoptive immunotherapy for cancer Dotti et al. (2014) Design and development of therapies using chimeric antigen receptor-expressing T cells Jensen and Riddell (2014) Design and implementation of adoptive therapy with chimeric antigen receptor-modified T cells Turtle et al. (2012) Engineered T cells for anti-cancer therapy

[13] [63] [64] [65]


Burst

Author

Year

Title

129

44.42

2011

Hyaluronic acid hydrogels for biomedical applications

ADV MATER

[66]

198

42.57

Burdick and Prestwich Ott et al.

2008

NAT MED

[67]

128

39.17

Dvir et al.

2011

NAT NANOTECHNOL

[68]

129 164

38.01 37.87

Macchiarini et al. Uygun et al.

2008 2010

Perfusion-decellularized matrix: using nature’s platform to engineer a bioartificial heart Nanotechnological strategies for engineering complex tissues Clinical transplantation of a tissue-engineered airway Organ reengineering through development of a transplantable recellularized liver graft using decellularized liver matrix

LANCET NAT MED

[69] [70]

with the strongest citation bursts. Title terms include hydrogels, transplantation, organ reengineering and decellularized matrix. In cluster #10, extracellular matrix, the only article that has referred to 13% of cluster members was written by Cassandra M. Kelleher in 2010, titled ‘engineering extracellular matrix through nanotechnology’ (http://rsif.royalsocietypublishing.org/content/7/Suppl_6/S717). Cluster #15 is labeled as organ transplantation. Table 10 lists the five articles in the cluster with the strongest citation bursts. The highest cited article in this cluster, Badylak2009 [36], discussed using extracellular matrix as a biological scaffold material. The common theme in terms of the citing articles to this cluster is the use of mesenchymal stromal cells for organ transplantation, including kidney and liver transplantations. 1308

Source

Ref.

Cluster #16 is on myocardial cell sheet therapy. Table 11 shows the five articles in the cluster with the strongest citation bursts. Bolli2011 [37] has the strongest citation burst in the cluster. It is a 2011 paper in Lancet on cardiac stem cells. A common theme among this group of articles appears to focus on applications of stem cell research to treat heart diseases. Three articles that cited the most members of the clusters are published in 2012 in the broader context of cardiac tissue engineering, cardiac cell therapy and cellular therapeutic applications. In terms of the silhouette values, the homogeneity of these clusters is generally considered very high in the range of 0.8 -- 0.9. In other words, the quality of the grouping is high. We also generated a larger network of 3331 cited references with Pathfinder networks based on top 500 citing articles per





Burst

115

35.44

104

Author

Year

Title

Source

Ref.

Lee et al.

2011

J R SOC INTERFACE

[71]

33.9

Crapo et al.

2011

BIOMATERIALS

[72]

193

33.8

Badylak et al.

2009

ACTA BIOMATER

[36]

128

29.44

Chen et al.

2010

BIOMATERIALS

[73]

76

27.06

Hoppe et al.

2011

Growth factor delivery-based tissue engineering: general approaches and a review of recent developments An overview of tissue and whole organ decellularization processes Extracellular matrix as a biological scaffold material: Structure and function Toward delivery of multiple growth factors in tissue engineering A review of the biological response to ionic dissolution products from bioactive glasses and glass-ceramics

BIOMATERIALS

[74]


Burst

164

64.43

177

Author

Year

Title

Bolli et al.

2011

52.88

Hare et al.

2009

118

46.34

Makkar et al.

2012

114

44.22

Williams and Hare

2011

119

35.12

Karussis et al.

2010

Cardiac stem cells in patients with ischaemic cardiomyopathy (SCIPIO): initial results of a randomised Phase I trial A randomized, double-blind, placebo-controlled, doseescalation study of intravenous adult human mesenchymal stem cells (prochymal) after acute myocardial infarction Intracoronary cardiosphere-derived cells for heart regeneration after myocardial infarction (CADUCEUS): a prospective, randomised Phase I trial Mesenchymal stem cells: biology, pathophysiology, translational findings, and therapeutic implications for cardiac disease Safety and immunological effects of mesenchymal stem cell transplantation in patients with multiple sclerosis and amyotrophic lateral sclerosis

year over the period from 2000 to 2014, in order to verify the sensitivity of the overview structure and especially recently formed clusters. The larger network revealed similar overall structure in which iPSC research is the most prominent and active area of activity. Newly emerged clusters include biomaterial, cancer, supramolecular hydrogel and organ transplantation. Notably, a small cluster #68, labeled as graphene, contains cited references on the use of nanomaterials in regenerative medicine. As a result, newly identified trends appear to focus on high-quality biomaterials and their applications in organ transplantation and other therapeutic applications in regenerative medicine. Cancer, heart, kidney and liver are among the most common research targets. Landmark articles and key concepts (2000 -- 2014): alluvial flow visualizations

4.3

Alluvial flow maps are designed to reveal temporal patterns in evolving networks [38]. To generate an alluvial map, the alluvial generator needs to have a series of networks as the input. Each network corresponds to the structure of an evolving network at a specific point of time. Each network is divided into a number of clusters. The corresponding clusters in adjacent networks

Source

Ref.

LANCET

[37]

J AM COLL CARDIOL

[75]

LANCET

[76]

CIRC RES

[77]

ARCH NEUROL-CHICAGO

[78]

form a sequence of views of how the same clusters evolve over time. The split and merge of thematic patterns can be visualized as multiple streams flowing smoothly over time. For this study, alluvial flow visualizations are generated as follows. CiteSpace is used in generating a series of individual networks of co-cited references or networks of co-occurring keywords, subject categories or noun phrases. These networks are exported from CiteSpace before they are loaded into the alluvial generator (http://www.mapequation.org/apps/AlluvialGenerator.html). Nodes in the import network that have the longest presence are highlighted by coloring the flows they form. Figure 10 shows an alluvial flow visualization of highly cited articles in the literature of regenerative medicine over a 15-year span. Individual networks were generated in CiteSpace as Pathfinder networks of references cited by top 100 citing articles in each of the 15 years. In addition, the five strongest connections per node and a 5-year look-back time were used as settings for network modeling. A flow in this visualization depicts the continuity of how an article was cited across the 15-year span. Flows may merge into a single flow or split into multiple flows. Four articles are highlighted in this example, namely Pittenger1999 [39], Evans1981 [40],


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Figure 10. An alluvial flow visualization of highly co-cited articles based on a 15-year period (2000 -- 2014) is shown. Each year’s network is formed by references made by top 100 citing articles and pruned by Pathfinder Network Scaling. For each node, the five strongest connections are retained. The look-back time is limited up to 5 years.

Figure 11. Alluvial flow of terms is shown. Each year top 100 noun phrases formed a Pathfinder network.

Takahashi2007 [35] and Stadtfeld2008 [9]. Pittenge1999 and Evans1981 have the longest non-interrupted presence in the literature of regenerative medicine. Both of their flows last for 14 years from 2001 to date. Takahashi2007 has a 7-year long flow since its first appearance in 2008. The flow of Stadtfeld2008 started in 2009 and lasted for 4 consecutive years. There are other articles that have long alluvial flows. Figure 11 shows an alluvial flow visualization of noun phrases extracted from titles and abstracts of publications in the literature of regenerative medicine. Individual networks were generated with CiteSpace as Pathfinder networks of top 100 noun phrases per time slice. The term ‘stem cells’ has the longest flow, spanning the entire 15 years. The term ‘bone marrow’ has a 14-year flow since 2001. Both of the terms ‘embryonic stem cells’ and ‘mesenchymal stem cells’ have 1310

a slightly shorter flow of 12 years since 2003. The terms ‘regenerative medicine’ and ‘tissue engineering’ have 9-year flows. The term ‘iPSCs’ has a 6-year flow since 2009. These term-based flows identify the dynamics of research focus. Interestingly, the flows of iPSCs and regenerative medicine merged in 2014, revealing that a strong co-occurring pattern of the two terms emerged in at this time.

Clusters of citing articles by bibliographic coupling

4.4

Networks of cited references and networks of citing articles offer insights from different perspectives. Network of cited references emphasize the perceived value of scientific publications in terms of how often they are cited by subsequent




Figure 12. A network of 6130 articles from the combined dataset of 71,393 one-step citation expansion is shown. Articles are grouped based on their bibliographic coupling. The largest four clusters are #0 mesenchymal stem cell, #1 DNA methylation, #2 transforming growth and #3 biomedical application.

Table 12. The largest five clusters in the 6130-article network. Cluster ID

Size

Silhouette

Mean (year)

0 1 2 3 4

180 158 149 147 137

0.999 1 1 1 1

2006 2013 2005 2011 2004

Label (TFIDF)

Label (LLR)

Label (MI)

Cumulus cell Action mechanism Systemic sclerosis Antibiotic Silico

Mesenchymal stem cell DNA methylation Transforming growth Biomedical application Silico

Depletion Corticofugal neuron Adult hippocampus Direct comparison Gene-expression profiling

LLR: Log-likelihood ratio; MI: Mutual information; TFIDF: Term frequency x inversed document frequency.

research. On the other hand, networks of citing articles can help us identify research topics based on various signs from the publications themselves. One way to organize citing articles into groups is to measure the similarity between two scientific publications in terms of similarities in how they cite the existing literature, namely bibliographic coupling. Other methods such as topic modeling may provide additional insights.

Figure 12 shows a network of citing articles based on bibliographic coupling. Clusters are identified and labeled using terms extracted from titles of the articles in these clusters. The largest cluster is #0 mesenchymal stem cell, located on the right-hand side of the visualization. It contains 180 articles with an average year of publication of 2006. Cluster #1 DNA methylation, near the top of the image, is not only the second-


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Figure 13. A dual-map overlay of dataset DA, which contains 3875 articles published between 2000 and 2011 is shown.

Figure 14. Dual-map overlays of the most-cited articles published between 2012 and 2014 on top of the DA overlay are shown. In particular, references made by 2012 articles are shown in green, references made by 2013 articles are shown in orange, and references made by 2014 articles are shown in red.

largest cluster of 158 articles but also the youngest one. Its average publication year is 2013. Cluster #3 is another young cluster with 147 articles and an average publication year of 2011 (Table 12). Dual-map overlays (2000 -- 2011 and 2012 -- 2014) The dual-map overlays are designed by Chen and Leydesdorff [24] to reveal patterns of a scientific portfolio with respect to a global map of scientific literature. The global basemap depicts the interconnections of over 10,000 scientific journals. These journals are further grouped into regions that represent 4.5

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publication and citation activities at a disciplinary level. The term ‘dual-map’ refers to the citing and cited component maps in the overall visualization. Given a set of articles, an overlay of the set visualizes the disciplinary concentrations of these articles and how they connect various regions in the global map through their citation links. Figure 13 shows a dual-map overlay visualization of articles published between 2000 and 2011 as a result of the topic search on regenerative medicine. This set of articles, DA, was the source of our 2011 review. Colored curves represent paths of references, originating from the citing map on the left and




Figure 15. Stadtfeld and Hochedlinger (2010) bridged three clusters #2, #5 and #12.

Table 13. Articles with the strongest structural variation potential score (weighted cluster linkage) based on structural properties during the period between 2006 and 2011. Cluster linkage Centrality Global weighted by divergence cites CR NR 2.0045

0.7845

223

1.4161

0.8324

12

1.3542

0.8828

131

1.2186

0.2678

21

1.0608

0.6138

10

1.0499

0.3606

11

0.9224

0.7125

22

0.8444

0.8619

10

0.8089

0.2216

11

0.7945

0.2826

12

Title

Reference

Induced pluripotency: history mechanisms and applications [9] Experimental approaches for the generation of induced pluripotent stem cells [79] Progress toward the clinical application of patient-specific pluripotent stem cells [41] The therapeutic potential of stem cells [80] An introduction to induced pluripotent stem cells [81] Distinguishing between mouse and human pluripotent stem cell regulation: the best laid plans of mice and men [82] The genetics of induced pluripotency [83] Roadblocks en route to the clinical application of induced pluripotent stem cells [84] Molecular characterization of the human NANOG protein [85] Cell reprogramming: expectations and challenges for chemistry in stem cell biology and regenerative medicine [86]

pointing to the cited map on the right. The positions of the starting and ending points of these curves tell us about how an article is built on previous work because both citing and cited maps are divided into a number of thematic areas and each position on the map belongs to one of the areas. The nature of each area is determined by a set of journals that belong to the area, that is, a cluster. Each area is labeled by the most-common words in the titles of the corresponding journals. The literature of regenerative medicine primarily appears in three broad areas on the citing map: the area in

Stadtfeld and Hochedlinger (2010) GENE DEV, V24, P2239 Sommer and Mostoslavsky (2010) STEM CELL RES THER, V1, P26 Kiskinis and Eggan (2010) J CLIN INVEST, V120, P51 Watt and Driskell (2010) PHILOS T R SOC B, V365, P155 Hanley et al. (2010) BRIT J HAEMATOL, V151, P16 Schnerch et al. (2010) STEM CELLS, V28, P419 Ralston and Rossant (2010) REPRODUCTION, V139, P35 Lowry and Quan (2010) J CELL SCI, V123, P643 Chang et al. (2009) STEM CELLS, V27, P812 Anastasia et al. (2010) CELL DEATH DIFFER, V17, P1230

pink near the top with the label of physics/materials/chemistry; the area in yellow in the middle with the label of molecular/biology/immunology and the area in green in the lower left corner with the label of medicine/medical/clinical. Citation curves that originated from each of the three primary regions point to regions in the cited map on the right. Citation links in green, for example, are split into a few major streams, indicating that publications in medicine- and clinical-related journals cite distinct groups of journals. Major destination regions for citations with the medicine origin


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include health/nursing/medicine, molecular/biology/genetics and environmental/toxicology/nutrition. As of February 2014, 48 articles published in 2012 received 20 or more citations. Similarly, 60 articles from 2013 have five or more citations, and six articles from 2014 were cited once. In order to highlight new developments from the publication point of view, additional overlays were added on top of the DA overlay for the three recent article sets (Figure 14). In particular, three overlays were added for these highly cited articles published in 2012 (green), 2013 (orange) and 2014 (red). The overlays suggest that the majority of these highly cited articles were published in 2013 in the molecular/biology/immunology journals. Structural variations: 2006 -- 2011 Structural variations measure the potential of a transformative research idea [25]. The dataset DB is a citation-based expansion of the literature of regenerative medicine (2000 -- 2014). We generated a predictive model of the global citations based on several structural variation metrics. For each year, the top 100 citing articles with 10 or more citations themselves were analyzed. For each node, the top 10 strongest connections were retained. The individual networks were pruned by Pathfinder Network Scaling. The model indicates that the cluster linkage measure is the best predictor of future citations with an incidence rate ratio of 5.546, which means a unit increase of the cluster linkage of an article is expected to lead to > 5 increases in the global citations of the article. According to this structural variation measure, Stadtfeld2010 [9] has the highest potential based on the structural properties in 2011, in part because it bridges multiple clusters #2, #5 and #12 (Figure 15). We discussed this article in our 2011 review [10]. As of the beginning of February 2014, it has been cited 223 times in the Web of Science (Table 13). The highly cited articles such as Stadtfeld2010 [9] and Kiskinis2010 [41] appear to bridge the basic research of iPSCs and potential clinical applications. We expect research in this category is likely to gain more attention in the near future. 4.6

5.

Expert opinion

This expert opinion is solely based on scientometric patterns revealed by CiteSpace without prior working experience in the regenerative medicine field. Emerging trends and new developments have been identified based on structural and temporal properties derived from the relevant publications. The detected surge of the keyword graphene in the literature of regenerative medicine led us to investigate the nature and context of its use in regenerative medicine. The investigation revealed a rapidly increasing number of studies that specifically used graphene and graphene oxide in regenerative medicine research because of their desirable surface properties for, among other applications, culturing and maintaining iPSCs [11]. Similarly a detected burst of citations provides insightful guidance for navigating through the fast-changing 1314

landscape of the relevant literature. For example, knowing exactly when citations to Takahashi2006 [6] had surged (2009 -- 2011) can improve our understanding of the complex adaptive behavior of the field. Knowing that Hanahan2011 [31] has been attracting much attention since 2012 can help us capitalize on the collective intelligence of the multidisciplinary scientific communities. The emergence of a new cluster indicates the beginning of a trend, for example, in organ transplantation. A persistent cluster represents a continuation of an existing trend, for example, in iPSCs. In our 2011 review, the literature of regenerative medicine was overwhelmingly predominated by the fast-moving research on iPSCs. Our 2011 analysis revealed two vibrant lines of research, namely, to further improve the efficiency of iPSC methods and to characterize clinical implications of iPSCs. In the new review, the iPSC research is still prominent. On the other hand, areas with strong clinical connections become increasingly visible. New and application-oriented research trends have emerged with the use of nanomaterials, graphene and cell sheet engineering. Many research topics in tissue engineering are establishing, or reestablishing, their positions in the new landscape of regenerative medicine. The recent publication of STAP [21] and its subsequently questioned reproducibility are so new that it would be too early to draw any conclusion. The tension between basic research and application-oriented engineering is likely to underscore the near future of regenerative medicine. The iPSC-related basic research, revitalized tissue engineering and graphene-stimulated new generations of nanomaterials are among the most active areas of research in regenerative medicine. Whereas research in iPSCs remains to be the most prominent area in the expanded literature of regenerative medicine, research related to clinical and therapeutic applications in regenerative medicine has experienced a considerable growth. In addition, clinical and therapeutic developments in regenerative medicine have profound connections with the current stem cell research, especially iPSCs. It is also worth noting that the increasingly prominent presence of tissue engineering in the new landscape of regenerative medicine does not mean that tissue engineering research was not significant enough before, rather, it was likely to be overshadowed by the sheer volume of iPSC-related research when we conducted our 2011 review and recent advances in iPSCs and the rapid adaptation of graphene-based nanomaterials have strengthened the already-existing connections. Nearly 2000 original papers and 670 review papers have been published since we conducted our previous review at the end of November 2011. In other words, the core literature of regenerative medicine has increased by 66.6 and 54.3% in terms of original research papers and review papers. The expanded dataset grew by > 5069 new publications, which is a growth rate of 7.5%. In contrast, the number of articles with the term ‘graphene’ on their list of keywords has increased 1600% since 2012. The shift of the intellectual landscape since




our 2011 review is significant -- the overwhelming domination of iPSC-related research has transformed to a field that is fundamentally influenced by a wider variety of research efforts, notably tissue engineering and clinical applications. Further, tissue engineering is transforming itself with revolutionary materials that improve the overall quality and performance of theoretical and practical research. The transformation from basic research such as iPSCs to application-oriented research such as tissue engineering and the use of nanomaterials may be a small step in the development of regenerative medicine. Other forms of macroscopic change may take place in the future, for example, leaving a mixture of basic research and application-oriented research and returning to a basic research-dominated phase. To keep abreast of rapid and

fundamental changes in the multidisciplinary field, it is essential to be able to maintain an updated picture of the field as a whole. The ability to conduct a systematic review at our own pace is important for us to maintain a sound understanding of where the field is going.

Declaration of interest The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents, received or pending, or royalties.

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Affiliation

Chaomei Chen†, Rachael Dubin & Meen Chul Kim † Author for correspondence Drexel University, College of Computing and Informatics, 3141 Chestnut Street, Philadelphia, PA 19104-2875, USA E-mail: [email protected]

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