A bio-mathematical model of time prediction in corneal angiogenesis after alkali burn
| Autor: | Zhengguo Wang, Kaifa Wang, Jianxin Jiang, Yijun Zeng, Pei-fang Zhu, Jun Yan |
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| Rok vydání: | 2007 |
| Předmět: |
Male
Vascular Endothelial Growth Factor A medicine.medical_specialty DNA Complementary Angiogenesis VEGF receptors Alkali burn Critical Care and Intensive Care Medicine Models Biological Polymerase Chain Reaction Performance index Back propagation neural network Mice Random Allocation Burns Chemical Linear regression medicine Animals Sodium Hydroxide Corneal Neovascularization Artificial neural network biology business.industry Corneal Angiogenesis General Medicine Surgery Eye Burns Emergency Medicine biology.protein RNA Female Nerve Net Thrombospondins business Biomedical engineering |
| Zdroj: | Burns. 33:511-517 |
| ISSN: | 0305-4179 |
| DOI: | 10.1016/j.burns.2006.08.029 |
| Popis: | Background The determination of angiogenesis time is the key prerequisite to obtaining a balance between valid repair and excessive angiogenesis in wound healing. The aim of the investigation was to establish a bio-mathematical model predicting corneal angiogenesis time after alkali burn by back propagation neural network (BP neural network). Methods The corneas of mice in 24 groups were burned by 0.01 mol/l NaOH. Five mice in each group were sacrificed at 6 h after alkali burn. The expression levels of vegf and tsp2, determined by real-time quantitive PCR, were used as input vectors in BP neural network. Meanwhile, the corneal angiogenesis of other mice, inspected every 3 h in 24 groups till the angiogenesis time were determined, served as output vectors. The data of 18 groups were randomly chosen for network adaptation while that of other 6 groups for simulation forecasting with functions of minmax (), postreg, prepca, trapca, respectively. Results A bio-mathematical model of two-level BP neural network was established, for its purpose to predict the angiogenesis time through the expression values of vegf and tsp2. The performance index (0.00999996) was smaller than the target value (0.01) after adapting 36,557 times and the accuracy rate of this predict system was 83.33%. Furthermore, the ideal regression line and the optimization regression line were almost coincident ( R = 0.988 in network adaptation and R = 0.793 in simulation forecasting). Conclusions The investigation indicated that the bio-mathematical model had available performance of simulation and forecasting. It might provide a novel method to solve clinical problems. |
| Databáze: | OpenAIRE |
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