Global trends and performances in diabetic retinopathy studies: A bibliometric analysis

Bibliometric analysis of publication trends on ocular hygiene and infections in the past two decades

Background

Ocular hygiene encompasses a spectrum of measures to initiate and maintain adequate ocular cleanliness to prevent eye infections and their further transmission. These infections affect all age groups and can lead to severe complications such as blindness. Nearly 1 billion cases could have been prevented out of over 2.2 billion people that are visually impaired worldwide. This bibliometric analysis focuses on the papers published on ocular hygiene and infections.

Methods

We searched in the Scopus database from 2004 to 2024. After manual screening, a list of the 100 most-cited original articles was obtained, which was analysed for various factors, including temporal trends, subject areas, authorship patterns, country of origin, funding bodies, etc.

Results

There has been a gradual decline over the past two decades in the number of publications. The United States was affiliated with the highest number of publications (n=61), followed by The United Kingdom (n=12) and Gambia (n=8). Several authors had 4 or more publications, with the highest number of publications by Rouse, B. T. (n=14). The American Journal of Ophthalmology takes the lead with 15 publications, while the National Eye Institute (n=23) was the leading funding body. Examination of institutional contributions shows that The University of Tennessee, Knoxville and UT College of Veterinary Medicine stand out with twelve publications each. Nearly half the publications belong to the field of medicine. However, significant publications also come from the fields Neuroscience, Microbiology and Immunology, Biochemistry, Genetics and Molecular Biology, etc. These findings highlight that there is great potential to conduct research to propagate ocular hygiene to prevent adverse effects of infections.

Bibliometric and visualized analysis of DME from 2012 to 2022

BACKGROUND

Diabetic macular edema (DME) is the main cause of irreversible vision loss in patients with diabetes mellitus (DM), resulting in a certain burden to patients and society. With the increasing incidence of DME, more and more researchers are focusing on it.

METHODS

The papers related to DME between 2012 and 2022 from the Web of Science core Collection were searched in this study. Based on CiteSpace and VOS viewer, these publications were analyzed in terms of spatiotemporal distribution, author distribution, subject classification, topic distribution, and citations.

RESULTS

A total of 5165 publications on DME were included. The results showed that the research on DME is on a steady growth trend. The country with the highest number of published documents was the US. Wong Tien Yin from Tsinghua University was the author with the most published articles. The journal of Retina, the Journal of Retinal and Vitreous Diseases had a large number of publications. The article "Mechanisms of macular edema: Beyond the surface" was the highly cited literature and "Aflibercept, bevacizumab, or ranibizumab for diabetic macular edema" had the highest co-citation frequency. The treatment, diagnosis, pathogenesis, as well as etiology and epidemiological investigation of DME, have been the current research direction. Deep learning has been widely used in the medical field for its strong feature representation ability.

CONCLUSIONS

The study revealed the important authoritative literature, journals, institutions, scholars, countries, research hotspots, and development trends in in the field of DME. This indicates that communication and cooperation between disciplines, universities, and countries are crucial. It can advance research in DME and even ophthalmology.

Neurovascular Relationships in AGEs-Based Models of Proliferative Diabetic Retinopathy

Diabetic retinopathy affects more than 100 million people worldwide and is projected to increase by 50% within 20 years. Increased blood glucose leads to the formation of advanced glycation end products (AGEs), which cause cellular and molecular dysfunction across neurovascular systems. These molecules initiate the slow breakdown of the retinal vasculature and the inner blood retinal barrier (iBRB), resulting in ischemia and abnormal angiogenesis. This project examined the impact of AGEs in altering the morphology of healthy cells that comprise the iBRB, as well as the effects of AGEs on thrombi formation, in vitro. Our results illustrate that AGEs significantly alter cellular areas and increase the formation of blood clots via elevated levels of tissue factor. Likewise, AGEs upregulate the expression of cell receptors (RAGE) on both endothelial and glial cells, a hallmark biomarker of inflammation in diabetic cells. Examining the effects of AGEs stimulation on cellular functions that work to diminish iBRB integrity will greatly help to advance therapies that target vision loss in adults.

A multidomain bio-inspired feature extraction and selection model for diabetic retinopathy severity classification: an ensemble learning approach

Diabetes retinopathy (DR) is one of the leading causes of blindness globally. Early detection of this condition is essential for preventing patients' loss of eyesight caused by diabetes mellitus being untreated for an extended period. This paper proposes the design of an augmented bioinspired multidomain feature extraction and selection model for diabetic retinopathy severity estimation using an ensemble learning process. The proposed approach initiates by identifying DR severity levels from retinal images that segment the optical disc, macula, blood vessels, exudates, and hemorrhages using an adaptive thresholding process. Once the images are segmented, multidomain features are extracted from the retinal images, including frequency, entropy, cosine, gabor, and wavelet components. These data were fed into a novel Modified Moth Flame Optimization-based feature selection method that assisted in optimal feature selection. Finally, an ensemble model using various ML (machine learning) algorithms, which included Naive Bayes, K-Nearest Neighbours, Support Vector Machine, Multilayer Perceptron, Random Forests, and Logistic Regression were used to identify the various severity complications of DR. The experiments on different openly accessible data sources have shown that the proposed method outperformed conventional methods and achieved an Accuracy of 96.5% in identifying DR severity levels.

What should be the future direction of development in the field of prostate cancer with lung metastasis?

BACKGROUND

Since the start of the 21st century, prostate cancer with lung metastasis (PCLM) has accumulated significant scientific research output. However, a systematic knowledge framework for PCLM is still lacking.

AIM

To reconstruct the global knowledge system in the field of PCLM, sort out hot research directions, and provide reference for the clinical and mechanism research of PCLM.

METHODS

We retrieved 280 high-quality papers from the Web of Science Core Collection and conducted a bibliometric analysis of keywords, publication volume, and citation frequency. Additionally, we selected differentially expressed genes from global high-throughput datasets and performed enrichment analysis and protein-protein interaction analysis to further summarize and explore the mechanisms of PCLM.

RESULTS

PCLM has received extensive attention over the past 22 years, but there is an uneven spatial distribution in PCLM research. In the clinical aspect, the treatment of PCLM is mainly based on chemotherapy and immunotherapy, while diagnosis relies on methods such as prostate-specific membrane antigen positron emission tomography/computed tomography. In the basic research aspect, the focus is on cell adhesion molecules and signal transducer and activator of transcription 3, among others. Traditional treatments, such as chemotherapy, remain the mainstay of PCLM treatment, while novel approaches such as immunotherapy have limited effectiveness in PCLM. This study reveals for the first time that pathways related to coronavirus disease 2019, cytokine-cytokine receptor interaction, and ribosome are closely associated with PCLM.

CONCLUSION

Future research should focus on exploring and enhancing mechanisms such as cytokine-cytokine receptor interaction and ribosome and improve existing mechanisms like cadherin binding and cell adhesion molecules.

Self-Citation Rates Among Neurosurgery Journals and Authors: Unethical or Misunderstood?

OBJECTIVE

The role of self-citation has not been discussed in the neurosurgery literature, although citations, citation indices, and impact of research may enhance funding opportunities, academic positions, fellowship opportunities, employment, and professional identity development. We sought to assess the magnitude and role of self-citation in academic neurosurgery.

METHODS

We performed a retrospective analysis of the citation and self-citation rates of articles published in 2001-2020 in 7 major neurosurgery journals: Acta Neurochirurgica, Journal of Neurosurgery, Journal of Neurosurgery: Pediatrics, Journal of Neurosurgery: Spine, Neurosurgery, Neurosurgical Review, and World Neurosurgery.

RESULTS

The total number of citations was highest for Journal of Neurosurgery and lowest for Neurosurgical Review. Journal of Neurosurgery: Spine had the highest average number of citations per article, followed closely by Journal of Neurosurgery. The self-citation rate increased for all journals over the time period 2001-2020. The highest number of self-citations per article during 2016-2020 was seen in Journal of Neurosurgery: Pediatrics and World Neurosurgery. Neurosurgical Review had the lowest number of self-citations per article.

CONCLUSIONS

Academic neurosurgeons must understand the ecosystem around self-citation. In our study, we found overall low levels of self-citations in neurosurgery journals with a few outliers. We have, however, noticed an increasing trend in self-citation rates. Self-citation rates should be considered while evaluating the impact of an author and research productivity. Contrary to popular belief, self-citation is not always unethical and must be understood within its circumstances.

Depiction of Branched-Chain Amino Acids (BCAAs) in Diabetes with a Focus on Diabetic Microvascular Complications

Type 2 diabetes mellitus (T2DM) still holds the title as one of the most debilitating chronic diseases with rising prevalence and incidence, including its complications such as retinal, renal, and peripheral nerve disease. In order to develop novel molecules for diagnosis and treatment, a deep understanding of the complex molecular pathways is imperative. Currently, the existing agents for T2DM treatment target only blood glucose levels. Over the past decades, specific building blocks of proteins-branched-chain amino acids (BCAAs) including leucine, isoleucine, and valine-have gained attention because they are linked with insulin resistance, pre-diabetes, and diabetes development. In this review, we discuss the hypothetical link between BCAA metabolism, insulin resistance, T2DM, and its microvascular complications including diabetic retinopathy and diabetic nephropathy. Further research on these amino acids and their derivates may eventually pave the way to novel biomarkers or therapeutic concepts for the treatment of diabetes and its accompanied complications.

Stachydrine Relieved the Inflammation and Promoted the Autophagy in Diabetes Retinopathy Through Activating the AMPK/SIRT1 Signaling Pathway

Background

Diabetes retinopathy (DR) is a chronic, progressive, and potentially harmful retinal disease associated with persistent hyperglycemia. Autophagy is a lysosome-dependent degradation pathway that widely exists in eukaryotic cells, which has recently been demonstrated to participate in the DR development. Stachydrine (STA) is a water-soluble alkaloid extracted from Leonurus heterophyllus. This study aimed to explore the effects of STA on the autophagy in DR progression in vivo and in vitro.

Methods

High glucose-treated human retinal microvascular endothelial cells (HRMECs) and STA-treated rats were used to establish DR model. The reactive oxygen species (ROS) and inflammatory factor levels (TNF-α, IL-1β, and IL-6) were determined using corresponding kits. Additionally, the cell growth was analyzed using CCK-8 and EdU assays. Besides, LC3BII, p62, p-AMPKα, AMPKα, and SIRT1 protein levels were measured using Western blot. The LC3BII and SIRT1 expressions were also determined using immunofluorescence.

Results

The results showed that STZ decreased the ROS and inflammatory factor levels in the HG-treated HRMECs. Besides, after STA treatment, the beclin-1, LC3BII, p-AMPKα, and SIRT1 levels were increased, and p62 was decreased in the HG-treated HRMECs and the retinal tissue of STZ-treated rats.

Conclusion

In conclusion, this study demonstrated that STA effectively relieved the inflammation and promoted the autophagy in DR progression in vivo and in vitro through activating the AMPK/SIRT1 signaling pathway.