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Application of Colorectal Cancer Animal Model:Based on Data Mining

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Author:
No author available
Journal Title:
Pharmacology and Clinics of Chinese Materia Medica
Issue:
6
DOI:
No doi available
Key Word:
结直肠癌;动物模型;数据挖掘;造模要素;氧化偶氮甲烷;葡聚糖硫酸钠;Colorectal cancer;Animal model;Data mining;Modeling elements;Azoxymethane(AOM);Dextran sulfate sodium(DSS)

Abstract: Objective:To study the application of animal models of colorectal cancer and provide a reference for improving the animal experiment method and animal model.Methods:With colorectal cancer and animal model as subject terms,we retrieved articles on animal models of colorectal cancer from China National Knowledge Infrastructure(CNKI),Wanfang Data,and PubMed(2001-2021)summarized the types and genders of experimental animals,modeling methods,cancer cell types,positive drugs,and detection indexes,and established a database for sta-tistical analysis.Results:A total of 274 eligible papers were screened out.The majority of the involved experimental animals were male BALB/c mice of 4-6 weeks old or(6-8)weeks old.The most frequently used modeling method was transplantation(generally by axillary in-oculation).As for the modeling with drugs,AOM/DSS were favored.Mouse CT-26 cell line and human LoVo cell line were common cells for the experiments.The 5-fluorouracil was frequently selected as the positive drug,generally with the dosage of 20 mg/kg and the administration usually lasted 4 weeks.The indexes of tumor tissue apparent index,mouse apparent index,tumor histopathology,immunohistochemistry result of tumor tissue,expression of tumor tissue proteins(Western blotting),and tumor inhibition rate were frequently adopted.Conclusion:A few methods for the animal modeling of colorectal cancer are available.It is suggested that traditional Chinese medicine should be involved in the modeling so as to improve the success rate of modeling,reproducibility of models,and similarity to the disease in clinical settings.This study evaluates different animal models of colorectal cancer based on data mining,which is expected to serve as a reference for model application and model improvement.

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