OBJECTIVE To investigate the effects of metformin （Met） on liver injury in non-alcoholic steatohepatitis （NASH） rats by regulating the PI3K/AKT/PDGF signaling pathway. METHODS NASH model was constructed by feeding rats with a high- glucose and high-fat diet， and assigned into Model group， Met low-dose group （Met-L group， 100 mg/kg）， Met medium-dose group （Met-M group， 200 mg/kg）， Met high-dose group （Met-H group， 400 mg/kg）， and high dose of Met+PI3K activator group （Met-H+740 Y-P group， 400 mg/kg Met+50 mg/kg 740 Y-P）， with 12 rats in each group. Another 12 rats were regarded as the Control group. Each group of rats was orally administered/injected with the corresponding medication once a day for 6 consecutive weeks. The changes in body weight and liver index of rats were recorded and analyzed. The pathological damage [evaluation of non-alcoholic fatty liver disease activity score （NAS）]， lipid deposition （calculation of the proportion of oil red O-positive staining area）， and fibrosis （calculation of collagen deposition score） were observed in liver tissue of rats. The levels of inflammatory factors [interleukin-6 （IL-6） and tumor necrosis factor-α （TNF-α）] in serum and liver tissue， the levels of serum lipid metabolism indicators [total cholesterol （TC）， triglyceride （TG）， and low-density lipoprotein cholesterol （LDL-C）] and liver function indicators [aspartate aminotransferase （AST） and alanine Δ 基金项目 武汉市知识创新专项项目（No.2022020801010588）； aminotransferase （ALT）] were measured. The expression levels of PI3K/AKT/PDGF signaling pathway-related proteins and Caspase-3 in liver tissue of rats were determined. RESULTS Compared with the Control group， body weight， liver index， the levels of serum lipid metabolism indicators and liver function indicators， the levels of IL-6 and TNF-α in serum and liver tissue， the NAS， the proportion of oil red O-positive staining area， the collagen deposition fraction， and the levels of phosphorylated PI3K and AKT proteins， as well as the expression levels of PDGF and Caspase-3 proteins in liver tissue， were all significantly increased （P＜0.05）. The liver tissue showed severe pathological damage， characterized by an abundance of lipid droplets and pronounced collagen deposition. After the intervention with Met， the aforementioned quantitative indicators and pathological changes in rats were significantly improved in a dose- dependent manner （P＜0.05）. 740 Y-P could reverse the improvement effects of high dose of Met on the above indexes of rats （P＜ 0.05）. CONCLUSIONS Met can improve liver damage， and alleviate inflammatory reactions and liver fibrosis of NASH rats， the mechanism of which may be associated with inhibiting PI3K/AKT/PDGF signaling pathway.

This study established a method for rapid quantification of terpene lactone, bilobalide, ginkgolide C, ginkgolide A and ginkgolide B in the chromatographic process of Ginkgo Folium based on near infrared spectroscopy(NIRS). The effects of competitive adaptive reweighting sampling(CARS), random frog(RF), and synergy interval partial least squares(siPLS) on the performance of partial least squares regression(PLSR) model were compared to the reference values measured by HPLC. Among them, the correlation coefficients of prediction(Rp) of validation sets of terpene lactone, bilobalide, and ginkgolide C were all higher than 0.98, and the relative standard errors of prediction(RSEPs) were 5.87%, 6.90% and 6.63%, respectively. Aiming at ginkgolide A and ginkgolide B with relatively low content, the genetic algorithm joint extreme learning machine(GA-ELM) was used to establish the optimized quantitative analysis model. Compared with CARS-PLSR model, the CARS-GA-ELM models of ginkgolide A and ginkgolide B exhibited a reduction in RSEP from 15.65% to 8.52% and from 21.28% to 10.84%, respectively, which met the needs of quantitative ana-lysis. It has been proved that NIRS can be used for the rapid detection of various lactone components in the chromatographic process of Ginkgo Folium.
Chromatography, High Pressure Liquid ; Ginkgo biloba ; Lactones/analysis* ; Least-Squares Analysis ; Spectroscopy, Near-Infrared/methods*

Based on near infrared spectroscopy and high performance liquid chromatography, this paper established the regression relationship between near infrared spectroscopy and index component content of Huoxiang Zhengqi oral liquid, so as to realize the rapid detection of index component content based on near infrared spectroscopy. Magnolol, honokiol and hesperidin were used as the quality indexes of Huoxiang Zhengqi oral liquid. After using the first derivative and normalization pretreatment method, characteristic variables were screened by CARS, and the correction model was finally established by partial least-squares regression (PLSR) method. The method accuracy was evaluated with the external validation, and the prediction results were tested for significance. The results indicated that when the near infrared spectrum was scanned through the bottle, the model's correlation coefficients of prediction (R_p) were higher than 0.99, the root mean square errors of the prediction models (RMSEP) were all less than 0.008 4, and the relative standard errors of prediction set (RSEP) were all less than 2.83%. There was no significant difference in the predicted results between these two kinds of model. The models established in the non-destructive way have good performance and high prediction accuracy. The rapid and nondestructive way has application value in the quality control of Huoxiang Zhengqi oral liquid.

Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.
Algorithms ; Ginkgo biloba ; Least-Squares Analysis ; Plant Leaves ; Spectroscopy, Near-Infrared