Bibliometric analysis of toll-like receptor agonists associated with cancer therapy

Toll-like receptors in health and disease

Toll-like receptors (TLRs) are inflammatory triggers and belong to a family of pattern recognition receptors (PRRs) that are central to the regulation of host protective adaptive immune responses. Activation of TLRs in innate immune myeloid cells directs lymphocytes to produce the most appropriate effector responses to eliminate infection and maintain homeostasis of the body's internal environment. Inappropriate TLR stimulation can lead to the development of general autoimmune diseases as well as chronic and acute inflammation, and even cancer. Therefore, TLRs are expected to be targets for therapeutic treatment of inflammation-related diseases, autoimmune diseases, microbial infections, and human cancers. This review summarizes the recent discoveries in the molecular and structural biology of TLRs. The role of different TLR signaling pathways in inflammatory diseases, autoimmune diseases such as diabetes, cardiovascular diseases, respiratory diseases, digestive diseases, and even cancers (oral, gastric, breast, colorectal) is highlighted and summarizes new drugs and related clinical treatments in clinical trials, providing an overview of the potential and prospects of TLRs for the treatment of TLR-related diseases.

Evaluation of the 101 most cited immunology articles: A bibliometric analysis

In our study, the 101 most cited publications in the field of "Immunology" in the Web of Science (WoS) database were evaluated bibliometrically. The "WoS" database "advanced mode" was used in this study. A search was conducted in the field of "Immunology." The top 101 most-cited publications were then identified. The total number of citations of the publications, annual number of citations, authors, information of the study, and journal were analyzed. There were 1,191,384 publications in the field of "Immunology" in the WoS between 1975 and 2023. The total citation average of the 101 most cited publications was 3765.69 ± 4142.7 and the annual citation average was 180.91 ± 133.1. The first 3 subjects in the first 101 articles were listed as "apoptosis-cancer immunology" (14.85%), "monocyte-macrophage"(14.58%), and "T-cell"(13.86%), respectively. The 3 journals in which the studies were published the most were determined as "Annual Review of Immunology" (18.81%), "Nature Reviews Immunology" (17.82%) and "Nature Immunology" (10.89%). Only 1.98% of publications were published after 2014. A significant relationship was found between the study type, impact factor of the journal, continent of journal and publisher, funding status, year of publication, and number of citations per year (P < .05). Our study is the first to analyze the top 101 most-cited studies in the field of "immunology" and their citation characteristics. The study's journals, publishers, publication features, funding status, and publication year affect the number of citations per year.

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.