The 100 top-cited articles in diabetic kidney disease: a bibliometric analysis

A Bibliometric Analysis of Research Articles on Midwifery Based on the Web of Science

Objective

This study aimed to bibliometrically analyse the main features of the 100 top-cited articles on the midwifery index on the Web of Science.

Methods

Academic articles on midwifery' research published from 1985 to 2020 were included. VOSviewer 1.6.15, SPSS 22.0 software and a homemade applet were used to identify, analyse and visualise the citation ranking, publication year, journal, country and organisation of origin, authorship, journal impact factor and keywords along with the total link strength of countries, organisations and keywords.

Results

Among the 100 top-cited articles, the highest number of citations of the retrieved articles was 484. The median number of citations per year was 5.16 (interquartile range: 3.74-8.38). Almost two-thirds of the included articles (n = 61) centred on nursing and obstetrics/gynaecology. The top-cited articles were published in 38 different journals, the highest number of which was published by Midwifery (15%). Australia was the most productive country (24%). According to the total link strength, the sequence ran from the United States (28) to England (28) to Australia (19). The University of Technology Sydney and La Trobe University in Australia topped the list with four papers each. Hunter B was the most productive author (n = 4), and the average citations were positively related to the number of authors (r = 0.336, p < 0.05).

Conclusion

This study identified the most influential articles on midwifery and documented the core journals and the most productive countries, organisations and authors along with future research hotspots for this field; the findings may be beneficial to researchers in their publication and scientific cooperation endeavours.

Citation trends in ophthalmology articles and keywords in mainland China, Hong Kong, and Taiwan since 2013 using temporal bar graphs (TBGs): Bibliometric analysis

BACKGROUND

We selected authors from mainland China, Hong Kong, and Taiwan (CHT) to examine citation trends on articles and keywords. The existence of suitable temporal bar graphs (TBGs) for displaying citation trends is unknown. It is necessary to enhance the traditional TBGs to provide readers with more information about the citation trend. The purpose of this study was to propose an advanced TBG that can be applied to understand the most worth-reading articles by ophthalmology authors in the CHT.

METHODS

Using the search engine of the Web of Science core collection, we conducted bibliometric analyses to examine the article citation trends of ophthalmology authors in CHT since 2013. A total of 6695 metadata was collected from articles and review articles. Using radar plots, the Y-index, and the combining the Y-index with the CJAL scores (CJAL) scores, we could determine the dominance of publications by year, region, institute, journal, department, and author. A choropleth map, a dot plot, and a 4-quadrant radar plot were used to visualize the results. A TBG was designed and provided for readers to display citation trends on articles and keywords.

RESULTS

We found that the majority of publications were published in 2017 (2275), Shanghai city (935), Sun Yat-Sen University (China) (689), the international journal Ophthalmology (1399), the Department of Ophthalmology (3035), and the author Peizeng Yang (Chongqing) (65); the highest CAJL scores were also from Guangdong (2767.22), Sun Yat-Sen University (China) (2147.35), and the Ophthalmology Department (7130.96); the author Peizeng Yang (Chongqing) (170.16) had the highest CAJL; and the enhanced TBG features maximum counts and recent growth trends that are not included in traditional TBGs.

CONCLUSION

Using the Y-index and the CJAL score compared with research achievements of ophthalmology authors in CHT, a 4-quadrant radar plot was provided. The enhanced TBGs and the CJAL scores are recommended for future bibliographical studies.

Research Trends in Advanced Glycation End Products and Obesity: Bibliometric Analysis

The aim of this study was to conduct a bibliometric analysis of the scientific articles on advanced glycation end products (AGEs) and obesity. English-language journal articles about AGEs and obesity were retrieved from the Scopus database. The OpenRefine application was used for data cleaning, the VOSviewer software program for analysis of the trends of year of publication, country, institution, journal, authors, references, and keywords. Microsoft Excel and Tableau Public were applied for the visualizing of the publication trends. Data collection was performed on 3 February 2022, from a total of 1170 documents. The Mann−Whitney test and Spearman test with software SPSS ver.28.0.1.1. were used to assess the relation between open access journal statuses, years of publications, and CiteScore. The results of the study showed that there was an increase in studies on processed foods, including AGEs and obesity. The United States was the country with the largest contribution in this field, with the highest number of citations. The Nutrients journal published the largest number of articles on this topic, particularly in the last two years. The present focus of the studies is on ultra-processed foods. The open access journals have younger medians of the year of publication and higher medians for number of citations than do closed access journals (p < 0.001 and p < 0.05, respectively). A strong negative association was seen between CiteScore and the year of publication (r = −0.64 [95% CI: −0.67, −0.60]), p < 0.001. We present this bibliometric analysis to furnish the most recent data on the description, visualization, and analysis of AGEs and obesity.

Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus

BACKGROUND

Diabetic kidney disease (DKD) is a common and serious complication in patients with diabetic mellitus (DM), the risk of cardiovascular events and all-cause mortality also increases in DKD patients. This study aimed to detect the influencing factors of DKD in type 2 DM (T2DM) patients, and construct DKD prediction models and nomogram for clinical decision-making.

METHODS

A total of 14,628 patients with T2DM were included. These patients were divided into pre-DKD and non-DKD groups, depending on the occurrence of DKD during a 3-year follow-up from first clinic attendance. The influencing indicators of DKD were analyzed, the prediction models were established by multivariable logistic regression, and a nomogram was drawn for DKD risk assessment.

RESULTS

Two prediction models for DKD were built by multivariate logistic regression analysis. Model 1 was created based on 17 variables using the forward selection method, Model 2 was established by 19 variables using the backward elimination method. The Somers' D values of both models were 0.789. Four independent predictors were selected to build the nomogram, including age, UACR, eGFR, and neutrophil percentages. The C-index of the nomogram reached 0.864, suggesting a good predictive accuracy for DKD development.

CONCLUSIONS

Our prediction models had strong predictive powers, and our nomogram provided visual aids to DKD risk calculation, which was simple and fast. These algorithms can provide early DKD risk prediction, which might help to improve the medical care for early detection and intervention in T2DM patients, and then consequently improve the prognosis of DM patients.

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.