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ChatGPT combined with big data analysis to analyze organoid research hotspots

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Organoids refer to tissue analogues with a certain spatial structure formed by using adult stem cells or pluripotent stem cells for in vitro three-dimensional culture. Although organoids are not really human organs, they can simulate real organs in structure and function, and can simulate the structure and function of tissues in vivo to the greatest extent and can be stably subcultured for a long time (hence also called "mini-organs"). As a tool, organoid technology has broad application prospects in basic research and clinical diagnosis and treatment research, including developmental biology, disease pathology, cell biology, precision medicine, and drug toxicity and efficacy testing. This technology also offers great potential for regenerative medicine, opening up the possibility of autologous or allogeneic cell therapy by replacing damaged or diseased tissues with organoid cultures.

Current hot spots and difficulties in organoid research include:

1. Accurate simulation of specific organ structure and function: At present, organoids have achieved remarkable results in neuroscience, cardiac biology, hepatology and other fields. Future developments will focus on further accurate simulation of the structure and function of specific organs, including tissue engineering of various cell types, simulation of blood supply, and biomechanical environment.

2. Functional enhancement and differentiation: Functional enhancement and differentiation of organoids is an important direction of research. By optimizing culture conditions and regulating cell signaling pathways and microenvironment, researchers are working to improve the tissue structure and function of organoids. Future development directions will focus on improving the cell differentiation and functional expression of organoids, and enhancing their similarity to real organs.

3. Cross-organ interactions: Interactions between different tissues and organs are crucial but difficult to study and control in vivo. Organoids provide an ideal platform for studying organ interactions. Future research directions will focus on the construction of cross-organ co-culture systems, simulating the complex interactions of the entire physiological system, and elucidating the mechanisms.

4. Disease models and drug screening: Organoids can be used for disease modeling and drug screening. Through gene editing technology and stem cell technology, researchers can introduce disease-related mutated genes into organoids to mimic the development of diseases. This provides an important platform for the study of disease mechanisms and the development of new drugs. Future development directions will focus on building more accurate disease models and using organoids for personalized medicine.

5. Bioprinting vs. Vascularization: Vascularization of organoids is key to long-term survival and better function. Bioprinting technology offers a possibility for the construction of organoids. The future development direction will explore the application of bioprinting technology in organoid construction to achieve better vascularization effect and improve the biocompatibility and functionality of organoids.

The above list is some of the common hotspots and future development directions of organoid research, but it should be noted that organoid research is a very broad field, and there are many other aspects that are worth exploring and studying in depth.

Thesis analysis

检索数据库:Medline

检索工具:文献鸟/PubMed

Retrieved on: July 8, 2024

Search term: Organoid

In the field of organoid research worldwide, 25,061 articles have been published in Medline; Literature Bird analyzed the latest 9994 articles.

Country distribution

ChatGPT combined with big data analysis to analyze organoid research hotspots

The distribution of countries shows that the number of articles published in the United States is 3775, accounting for 37.8% of the total, ranking first; The number of articles published by Chinese authors was 1864, accounting for 18.7%; Germany, Japan and the Netherlands ranked 3rd to 5th in terms of the number of articles published.

Academic Institution Rankings

ChatGPT combined with big data analysis to analyze organoid research hotspots

Utrecht University Medical Center in the Netherlands published the most articles (71 papers), but the most influential academic was Stanford University School of Medicine (1246.2 papers), and the universities with the largest number of published articles were the University of California, San Francisco (65 papers), University of California, San Diego (65 papers), Kyoto University (54 papers), University of Cambridge (54 papers), University of Pennsylvania (49 papers), University of Hong Kong (47 papers), and Harvard Medical School (46 papers) , Fudan University (46 papers), Southern Medical University (44 papers), Stanford University (44 papers), Baylor College of Medicine (43 papers), Johns Hopkins University School of Medicine (43 papers), Keio University School of Medicine (42 papers), Cincinnati Children's Hospital Medical Center (41 papers), etc.

Hospital Rankings

ChatGPT combined with big data analysis to analyze organoid research hotspots

The Cincinnati Children's Hospital Medical Center published the largest number of articles with 59 articles, followed by Utrecht University Medical Center (58 articles), Mayo Clinic (37 articles), Massachusetts General Hospital (36 articles), West China Hospital (31 articles), South China Hospital (27 articles), and Leiden University Medical Center (26 articles).

Published journals

ChatGPT combined with big data analysis to analyze organoid research hotspots

发表类器官研究领域稿件数量较多的期刊主要有Int J Mol Sci(IF=4.9)(271篇)、Nat Commun (IF=14.7)(243篇)、Cells(IF=5.1)(183篇)、Front Cell Dev Biol (IF=4.6)(174篇)、Cancers (Basel) (IF=4.5)(151篇)、Sci Rep (IF=3.8)(150篇)等。

The most active academics

ChatGPT combined with big data analysis to analyze organoid research hotspots

Prof. Clevers, Hans of Medical Center of Japan 庆应义塾大学, Prof. Pașca, Sergiu P of 美国斯坦福 University, Prof. Su, Jiacan of the Institute of Medicine of Shanghai University, Prof. Lancaster, Madeline A, etc.

Deficiencies of this analysis:

If the spelling is different, the database will treat the same unit, the same fund, etc. as two different units.

Due to the limitations of the search, there is likely to be inappropriate data analysis; I would like to ask the experts for their corrections.

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