Value of second harmonic generation/two-photon excitation fluorescence in quantitative evaluation of liver fibrosis in mice with nonalcoholic fatty liver disease

• Main Author: WANG Xiaoxiao
• Format: Article
• Language: zho
• Published: Editorial Department of Journal of Clinical Hepatology 2019-08-01
• Series: Linchuang Gandanbing Zazhi
• Online Access: http://www.lcgdbzz.org/qk_content.asp?id=10097

Objective To investigate the dynamic changes in collagen parameters in mice with nonalcoholic fatty liver disease (NAFLD) by second harmonic generation (SHG)/two-photon excitation fluorescence (TPEF), to establish the parameters for automatic quantitative evaluation of mice with NAFLD induced by methionine- and choline-deficient diet (MCD), and to provide an experimental basis for the application of SHG/TPEF in clinical practice. Methods Liver tissue specimens of MCD mice were collected at weeks 0, 4, 8, 12, 16, 20, and 24, and HE staining, Masson staining, and sirius red (SR) staining were performed. Collagen proportionate area (CPA) and hydroxyproline (HYP) were calculated. SHG/TPEF was used to analyze 100 collagen parameters. With time points and fibrosis stage (S0-S4) as criteria, the SVM model was used to analyze the collagen parameters. A receiver operating characteristic (ROC) curve analysis was performed for the collagen parameters, and these parameters were compared with CPA and HYP in terms of the area under the ROC curve (AUC). Results In the MCD mice, HE and SR staining showed that over the time of modeling, hepatic steatosis was observed in hepatic lobules, with gradual aggravation of fibrosis. A total of 26 parameters were screened out based on their correlation with time point, and 27 parameters were screened out based on their correlation with liver fibrosis stage. The analysis based on the SVM model identified 7 shared parameters (#StrCV, #ShortStrCV, #ThickStrCV, #StrPTAgg, #StrPSAgg, #LongStrPSAgg, and StrLengthPSAgg). These 7 parameters had an AUC of 0.857-0.923 (P＜0.05) in predicting different stages of liver fibrosis and an AUC of 0.823 -0.976 (P＜0.05) in predicting liver fibrosis at different time points. These 7 parameters were compared with CPA and HYP in terms of their value in predicting liver fibrosis, and it was found that the 7 parameters had a similar AUC as CPA and HYP in predicting S0 fibrosis, while the 7 parameters had a significantly higher AUC than CPA and HYP in predicting S1-S4 fibrosis. The 7 parameters had a similar AUC as CPA and HYP at week 0 of modeling, while at week 4 of modeling, the 7 parameters had a significantly higher AUC than CPA and HYP. Conclusion Seven parameters associated with fibrosis stage and time point can accurately reflect the changes in liver fibrosis in different stages and at different time points in a model of MCD-induced NAFLD, and therefore, they can be used for specific and accurate monitoring of liver fibrosis in a quantitative manner in this model.