Objective:To investigate the effects of hydroxysafflor yellow A (HSYA) on depressive-like behavior and expression of type A γ-aminobutyric acid receptor(GABAAR)in hippocampus of chronic restraint stress model mice.Methods:The SPF grade male C57BL/6C mice were divided into Control group, HSYA group, Model group, Model + HSYA group and Model + fluoxetine group according to random number table method, with 12 mice in each group.Mice model of depression was established by chronic restraint stress.Mice in HSYA group and Model+ HSYA group were intraperitoneally injected with HSYA(20 mg/kg), mice in Model+ fluoxetine group were injected intraperitoneally with fluoxetine (10 mg/kg), and mice in Control group and Model group administered with 0.9% sodium chloride solution intraperitoneally once a day for 14 days.Then, the forced swimming test (FST) and tail suspension test (TST) were performed to evaluate the depressive-like behavior of mice, and the protein expression levels of different subtypes of GABAAR in the hippocampus of mice were determined by Western blot.SPSS 19.0 and GraphPad Prism 8.0 software were used for data statistical analysis and mapping.One-way ANOVA was used for comparison among groups, and Tukey-HSD test was used for further pairwise comparison.Results:(1) In the behavioral tests, there were significant differences in swimming immobility time of FST and tail suspension immobility time of TST among the five groups ( F=21.59, 20.81, both P<0.05). The swimming immobility time ((143.91±9.97) s) and tail suspension immobility time (( 107.00±6.54) s) in Model group were higher than those in Control group ((52.92±6.70) s, ( 43.50±5.96) s, both P<0.05). There were no significant difference in swimming immobility time and tail suspension immobility time between Model+ HSYA group ((26.17±7.69)s, ( 20.17±7.89)s) and Model+ fluoxetine group ((61.60±16.22)s, (34.14±10.74)s)(both P>0.05), but the swimming immobility time and tail suspension immobility time in these two groups were lower than those in Model group (both P<0.05). (2) The Western blot results showed that there were significant differences in the expression of GABAARβ1 and GABAARβ2 protein in hippocampus among the four groups ( F=12.21, 11.40, both P<0.05). The expression levels of GABAARβ1(45.60±10.76) and GABAARβ2 (46.27±4.82) protein in hippocampus of Model group were lower than those in Control group ((100.00±3.44), (100.00±3.26), both P<0.05). Compared to Model group, the expression of GABAARβ1 (79.91±5.00) and GABAARβ2 (79.08±5.53) protein in hippocampus of Model+ HSYA group were higher (both P<0.05). In addition, the expression of GABAARα1 and GABAARγ1 proteins in hippocampus were not significantly different among the four groups( F=0.23, 0.10, both P>0.05). Conclusion:HSYA can effectively alleviate depressive-like behavior in depression model mice, which may be related with the upregulation of GABAARβ1 and GABAARβ2 of hippocampus tissue.

Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.