Gastric Cancer: Bibliometric Analysis of Epidemiological, Geographical and Socio-Economic Parameters of the Global Research Landscape

Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis

Background

Interest in critical care–related artificial intelligence (AI) research is growing rapidly. However, the literature is still lacking in comprehensive bibliometric studies that measure and analyze scientific publications globally.

Objective

The objective of this study was to assess the global research trends in AI in intensive care medicine based on publication outputs, citations, coauthorships between nations, and co-occurrences of author keywords.

Methods

A total of 3619 documents published until March 2022 were retrieved from the Scopus database. After selecting the document type as articles, the titles and abstracts were checked for eligibility. In the final bibliometric study using VOSviewer, 1198 papers were included. The growth rate of publications, preferred journals, leading research countries, international collaborations, and top institutions were computed.

Results

The number of publications increased steeply between 2018 and 2022, accounting for 72.53% (869/1198) of all the included papers. The United States and China contributed to approximately 55.17% (661/1198) of the total publications. Of the 15 most productive institutions, 9 were among the top 100 universities worldwide. Detecting clinical deterioration, monitoring, predicting disease progression, mortality, prognosis, and classifying disease phenotypes or subtypes were some of the research hot spots for AI in patients who are critically ill. Neural networks, decision support systems, machine learning, and deep learning were all commonly used AI technologies.

Conclusions

This study highlights popular areas in AI research aimed at improving health care in intensive care units, offers a comprehensive look at the research trend in AI application in the intensive care unit, and provides an insight into potential collaboration and prospects for future research. The 30 articles that received the most citations were listed in detail. For AI-based clinical research to be sufficiently convincing for routine critical care practice, collaborative research efforts are needed to increase the maturity and robustness of AI-driven models.

Global research trends in atherosclerosis: A bibliometric and visualized study

Background

Increasing evidence has spurred a considerable evolution of concepts related to atherosclerosis, prompting the need to provide a comprehensive view of the growing literature. By retrieving publications in the Web of Science Core Collection (WoSCC) of Clarivate Analytics, we conducted a bibliometric analysis of the scientific literature on atherosclerosis to describe the research landscape.

Methods

A search was conducted of the WoSCC for articles and reviews serving exclusively as a source of information on atherosclerosis published between 2012 and 2022. Microsoft Excel 2019 was used to chart the annual productivity of research relevant to atherosclerosis. Through CiteSpace and VOSviewer, the most prolific countries or regions, authors, journals, and resource-, intellectual-, and knowledge-sharing in atherosclerosis research, as well as co-citation analysis of references and keywords, were analyzed.

Results

A total of 20,014 publications were retrieved. In terms of publications, the United States remains the most productive country (6,390, 31,93%). The most publications have been contributed by Johns Hopkins Univ (730, 3.65%). ALVARO ALONSO produced the most published works (171, 0.85%). With a betweenness centrality of 0.17, ERIN D MICHOS was the most influential author. The most prolific journal was identified as Atherosclerosis (893, 4.46%). Circulation received the most co-citations (14,939, 2.79%). Keywords with the ongoing strong citation bursts were “nucleotide-binding oligomerization (NOD), Leucine-rich repeat (LRR)-containing protein (NLRP3) inflammasome,” “short-chain fatty acids (SCFAs),” “exosome,” and “homeostasis,” etc.

Conclusion

The research on atherosclerosis is driven mostly by North America and Europe. Intensive research has focused on the link between inflammation and atherosclerosis, as well as its complications. Specifically, the NLRP3 inflammasome, interleukin-1β, gut microbiota and SCFAs, exosome, long non-coding RNAs, autophagy, and cellular senescence were described to be hot issues in the field.

Pretreatment Contrast-Enhanced Computed Tomography Radiomics for Prediction of Pathological Regression Following Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer: A Preliminary Multicenter Study

Background

Sensitivity to neoadjuvant chemotherapy in locally advanced gastric cancer patients varies; however, an effective predictive marker is currently lacking. We aimed to propose and validate a practical treatment efficacy prediction method based on contrast-enhanced computed tomography (CECT) radiomics.

Method

Data of l24 locally advanced gastric carcinoma patients who underwent neoadjuvant chemotherapy were acquired retrospectively between December 2012 and August 2020 from three different cancer centers. In total, 1216 radiomics features were initially extracted from each lesion’s pretreatment portal venous phase computed tomography image. Subsequently, a radiomics predictive model was constructed using machine learning software. Clinicopathological data and radiological parameters of the enrolled patients were collected and analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to screen for independent predictive indices. Finally, we developed an integrated model combining clinicopathological predictive parameters and radiomics features.

Result

In the training set, 10 (14.9%) patients achieved a good response (GR) after preoperative neoadjuvant chemotherapy (n = 77), whereas in the testing set, seven (17.5%) patients achieved a GR (n = 47). The radiomics predictive model showed competitive prediction efficacy in both the training and independent external validation sets. The areas under the curve (AUC) values were 0.827 (95% confidence interval [CI]: 0.609–1.000) and 0.854 (95% CI: 0.610–1.000), respectively. Similarly, when only the single hospital data were included as an independent external validation set (testing set 2), AUC values of the models were 0.827 (95% CI: 0.650–0.952) and 0.889 (95% CI: 0.663–1.000) in the training set and testing set 2, respectively.

Conclusion

Our study is the first to discover that CECT radiomics could provide powerful and consistent predictions of therapeutic sensitivity to neoadjuvant chemotherapy among gastric cancer patients across different hospitals.

Association between WNT-1-inducible signaling pathway protein-1 (WISP1) genetic polymorphisms and the risk of gastric cancer in Guangxi Chinese

Background

WNT1-inducible signaling pathway protein 1 (WISP1) is a member of the CCN protein family and a downstream target of β-catenin. Aberrant WISP1 expression may be involved in carcinogenesis. To date, no studies have investigated the association between single-nucleotide polymorphisms (SNPs) of WISP1 and gastric cancer. Therefore, we conducted this study to explore their relationship.

Methods

Polymerase chain reaction-restriction fragment length polymorphism assay was used to analyze three SNPs of WISP1 in 204 gastric cancer patients and 227 controls.

Results

Overall, we could not identify a significant association between WISP1 SNPs and gastric cancer risk. However, the subgroup analysis demonstrated that the presence of the rs7843546 T allele was associated with a significantly decreased risk of gastric cancer in those of Han Chinese ethnicity (CT vs. CC: OR = 0.33, 95%CI 0.14–0.78; TT vs. CC: OR = 0.29, 95%CI 0.11–0.76; CT + TT vs. CC: OR = 0.32, 95%CI 0.14–0.74). In addition, patients with the rs7843546 TT genotype display a 0.34-fold lower risk of developing stage I/II gastric cancer than those with the CC genotype Furthermore, individuals ≥ 50 years old who carried the rs10956697 AC genotype had a significantly decreased risk of gastric cancer (OR = 0.58, 95%CI 0.35–0.98). Smokers with the rs10956697 AC and AC + AA genotypes exhibited a 0.28-fold lower and 0.32-fold lower risk of gastric cancer, respectively.

Conclusions

The WISP1 SNPs rs7843546 and rs10956697 were, for the first time, found to reduce susceptibility to gastric cancer in various subgroups of Guangxi Chinese.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02116-2.