功能性消化不良病证结合动物模型研究进展
Research progress on animal models of functional dyspepsia combined with syndrome differentiation
邓娴 1谭瑾轩 1陈映西 1潘娜 1刘付杰义 1黄晓燕2
作者信息
- 1. 广西中医药大学研究生院,广西 南宁 530001
- 2. 广西中医药大学第一附属医院,广西 南宁 530022
- 折叠
摘要
功能性消化不良(functional dyspepsia,FD)是常见无器质性病变的消化系统疾病,发病机制尚未明确,治疗方案策略亟待进一步探索,建立合适的FD病证结合动物模型有助于进一步深入探讨疾病的发病机制和评价中药方剂的治疗效果.本文对FD常见中医证型脾虚证、肝郁证、肝郁脾虚证常用动物造模方法及评价指标进行整理分析.分析发现造模过程中结合饮食不节法或劳倦过度法更利于模型的稳定.脾虚是FD的发病基础,设计合适的FD脾虚动物造模方法或可为治疗FD提供新的治疗方向和更好的治疗策略.现有FD病证结合模型评价指标普遍存在主观性过强、客观证据不足的问题,如何完善FD病证结合动物模型评价系统是未来努力的方向.
Abstract
Functional dyspepsia(FD)is a common digestive system disease with unclear pathogenesis and no organic lesions.The treatment plan and strategy urgently need to be further explored.Establishing a mature and complete animal model combining FD disease and syndrome can help further explore the pathogenesis of the disease and evaluate the therapeutic effect of traditional Chinese medicine formulas.This study aims to analyze the commonly used animal modeling methods and evaluation indicators for spleen deficiency syndrome,liver depression syndrome,and liver depression spleen deficiency syndrome in FD.The combination of improper diet or excessive fatigue during the modeling process is more conducive to the stability of the model,which is related to modern lifestyle habits.Spleen deficiency is the foundation of FD.Designing mature and complete animal modeling methods for spleen deficiency in FD may provide new treatment directions and better treatment strategies for treating FD.The existing evalua-tion indicators for the combination of FD disease and syndrome models generally have strong subjectivity and insufficient objective evidence.How to improve the evaluation system for the combination of FD disease and syndrome animal models is a direction that needs to be worked on in the future.
关键词
功能性消化不良/动物模型研究/病证结合/中医证型/研究进展Key words
Functional dyspepsia/Animal model research/Combination of disease and syndrome/TCM syndrome/Re-search progress引用本文复制引用
基金项目
广西壮族自治区创新驱动发展专项(桂科AA17202031)
出版年
2024
国家科技期刊平台
NSTL
万方数据