Visual analysis on the research of monocarboxylate transporters based on CiteSpace

Research Hotspots and Trends on Acupuncture for Anti-Inflammation: A Bibliometric Analysis from 2011 to 2021

Purpose

We here explored the research status, research hotspots, and development trend of acupuncture against inflammation from both quantitative and qualitative aspects through bibliometrics.

Methods

We used CiteSpace and VOSviewer to analyze the literature about acupuncture against inflammation from 2011 to 2021 in the Web of Science Core Collection database by using a visual knowledge map.

Results

In total, 1479 articles were included, and the number of articles published each year exhibited an upward trend. The largest number of articles were published in China (661), followed by the United States (287) and South Korea (164). The most productive institution is Beijing University of Chinese Medicine (72), while the most influential institution is the Capital Medical University (0.28). Evidence-based Complementary and Alternative Medicine (131) is the journal that published most articles on the topic. Lin Yiwen is the most prolific author, and Borovikova L is the most influential co-cited author. The keywords that have burst in the last 2 years are inflammation and activation. The keywords with the highest frequency of use are electroacupuncture (EA), inflammation, and expression.

Conclusion

The number of publications on acupuncture for anti-inflammation research is rapidly increasing. China is a productive country, but the influence of centrality is poor. Research institutions are concentrated in universities, and the whole collaborative network needs to be strengthened. The anti-inflammatory mechanism of acupuncture is the main focus of research in this field. Regulation of immune cell balance by acupuncture may be a hot topic in mechanism research. At present, immune cells, vagus nerve, signal pathway, inflammatory corpuscles, cytokines and neurotransmitters are popular research topics. In the future, the basic research of acupuncture for anti-inflammation transformed into clinical practice may be a trend. EA and bee venom acupuncture may be promising research directions for acupuncture treatment for inflammatory diseases.

Trend and prediction of citations on the topic of neuromuscular junctions in 100 top-cited articles since 2001 using a temporal bar graph: A bibliometric analysis

BACKGROUND

A neuromuscular junction (NMJ) (or myoneural junction) is a chemical synapse between a motor neuron (MN) and a muscle fiber. Although numerous articles have been published, no such analyses on trend or prediction of citations in NMJ were characterized using the temporal bar graph (TBG). This study is to identify the most dominant entities in the 100 top-cited articles in NMJ (T100MNJ for short) since 2001; to verify the improved TBG that is viable for trend analysis; and to investigate whether medical subject headings (MeSH terms) can be used to predict article citations.

METHODS

We downloaded T100MNJ from the PubMed database by searching the string ("NMJ" [MeSH Major Topic] AND ("2001" [Date - Modification]: "2021" [Date - Modification])) and matching citations to each article. Cluster analysis of citations was performed to select the most cited entities (e.g., authors, research institutes, affiliated countries, journals, and MeSH terms) in T100MNJ using social network analysis. The trend analysis was displayed using TBG with two major features of burst spot and trend development. Next, we examined the MeSH prediction effect on article citations using its correlation coefficients (CC) when the mean citations in MeSH terms were collected in 100 top-cited articles related to NMJ (T100NMJs).

RESULTS

The most dominant entities (i.e., country, journal, MesH term, and article in T100NMJ) in citations were the US (with impact factor [IF] = 142.2 = 10237/72), neuron (with IF = 151.3 = 3630/24), metabolism (with IF = 133.02), and article authored by Wagh et al from Germany in 2006 (with 342 citing articles). The improved TBG was demonstrated to highlight the citation evolution using burst spots, trend development, and line-chart plots. MeSH terms were evident in the prediction power on the number of article citations (CC = 0.40, t = 4.34).

CONCLUSION

Two major breakthroughs were made by developing the improved TBG applied to bibliographical studies and the prediction of article citations using the impact factor of MeSH terms in T100NMJ. These visualizations of improved TBG and scatter plots in trend, and prediction analyses are recommended for future academic pursuits and applications in other disciplines.

Using the alluvial plot to visualize the network characteristics of 100 top-cited articles on attention-deficit/hyperactivity disorder (ADHD) since 2011: Bibliometric analysis

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a common neuro developmental disorder that affects children and adolescents. It is estimated that the prevalence of ADHD is 7.2% throughout the world. There have been a number of articles published in the literature related to ADHD. However, it remains unclear which countries, journals, subject categories, and articles have the greatest influence. The purpose of this study was to display influential entities in 100 top-cited ADHD-related articles (T100ADHD) on an alluvial plot and apply alluvial to better understand the network characteristics of T100ADHD across entities.

METHODS

Using the PubMed and Web of Science (WoS) databases, T100ADHD data since 2011 were downloaded. The dominant entities were compared using alluvial plots based on citation analysis. Based on medical subject headings (MeSH terms) and research areas extracted from PubMed and WoS, social network analysis (SNA) was performed to classify subject categories. To examine the difference in article citations among subject categories and the predictive power of MeSH terms on article citations in T100ADHD, one-way analysis of variance and regression analysis were used.

RESULTS

The top 3 countries (the United States, the United Kingdom, and the Netherlands) accounted for 75% of T100ADHD. The most citations per article were earned by Brazil (=415.33). The overall impact factor (IF = citations per 100) of the T100ADHD series is 188.24. The most cited article was written by Polanczyk et al from Brazil, with 772 citations since 2014. The majority of the articles were published and cited in Biol Psychiatry (13%; IF = 174.15). The SNA was used to categorize 6 subject areas. On the alluvial plots, T100ADHD's network characteristics were successfully displayed. There was no difference in article citations among subject categories (F = 1.19, P = .320). The most frequently occurring MeSH terms were physiopathology, diagnosis, and epidemiology. A significant correlation was observed between MeSH terms and the number of article citations (F = 25.36; P < .001).

CONCLUSION

Drawing the alluvial plot to display network characteristics in T100ADHD was a breakthrough. Article subject categories can be classified using MeSH terms to predict T100ADHD citations. Bibliometric analyses of 100 top-cited articles can be conducted in the future.