Mapping global trends in research of stem cell therapy for COVID-19: A bibliometric analysis

Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace

BACKGROUND

The advancements in artificial intelligence (AI) technology for image recognition were propelling molecular pathology research into a new era.

OBJECTIVE

To summarize the hot spots and research trends in the field of molecular pathology image recognition.

METHODS

Relevant articles from January 1st, 2010, to August 25th, 2023, were retrieved from the Web of Science Core Collection. Subsequently, CiteSpace was employed for bibliometric and visual analysis, generating diverse network diagrams illustrating keywords, highly cited references, hot topics, and research trends.

RESULTS

A total of 110 relevant articles were extracted from a pool of 10,205 articles. The overall publication count exhibited a rising trend each year. The leading contributors in terms of institutions, countries, and authors were Maastricht University (11 articles), the United States (38 articles), and Kather Jacob Nicholas (9 articles), respectively. Half of the top ten research institutions, based on publication volume, were affiliated with Germany. The most frequently cited article was authored by Nicolas Coudray et al. accumulating 703 citations. The keyword "Deep learning" had the highest frequency in 2019. Notably, the highlighted keywords from 2022 to 2023 included "microsatellite instability", and there were 21 articles focusing on utilizing algorithms to recognize microsatellite instability (MSI) in colorectal cancer (CRC) pathological images.

CONCLUSION

The use of DL is expected to provide a new strategy to effectively solve the current problem of time-consuming and expensive molecular pathology detection. Therefore, further research is needed to address issues, such as data quality and standardization, model interpretability, and resource and infrastructure requirements.

The Application of Deep Brain Stimulation for Parkinson's Disease on the Motor Pathway: A Bibliometric Analysis across 10 Years

BACKGROUND AND OBJECTIVE

Since its initial report by James Parkinson in 1817, Parkinson's disease (PD) has remained a central subject of research and clinical advancement. The disease is estimated to affect approximately 1% of adults aged 60 and above. Deep brain stimulation, emerging as an alternative therapy for end-stage cases, has offered a lifeline to numerous patients. This review aimed to analyze publications pertaining to the impact of deep brain stimulation on the motor pathway in patients with PD over the last decade.

METHODS

Data were obtained from the Web of Science Core Collection through the library of Huazhong University of Science and Technology (China). The search strategy encompassed the following keywords: "deep brain stimulation", "Parkinson's disease", "motor pathway", and "human", from January 1, 2012, to December 1, 2022. Additionally, this review visualized the findings using the Citespace software.

RESULTS

The results indicated that the United States, the United Kingdom, Germany, and China were the primary contributors to this research field. University College London, Capital Medical University, and Maastricht University were the top 3 research institutions in the research area. Tom Foltynie ranked first with 6 publications, and the journals of Brain and Brain Stimulation published the greatest number of relevant articles. The prevailing research focal points in this domain, as determined by keywords "burst analysis", "encompassed neuronal activity", "nucleus", "hyper direct pathway", etc. CONCLUSION: This study has provided a new perspective through bibliometric analysis of the deep brain stimulation therapy for treating patients with PD, which can shed light on future research to advance our comprehension of this particular field of study.