Objective To investigate the changes of expression of stromal cell-derived factor 1α (SDF-1α) in retinas after partial optic nerve injury in rats. Methods Models with injury of partial optic nerve were induced in rats. Retinal tissues were collected 1,2,3,5,7,10 and 14 d after injury. Enzyme linked immunosorbent assay and Real-time quantitative PCR were employed to detect the expression of SDF-1α protein and mRNA in retinal tissues respectively in injury group (n=28), sham operated group (n=28) and normal control group (n=12). Results The expression of SDF-1α protein and mRNA in retinas was higher than that in sham operated group and normal control group at different time points after injury (P<0.01), and it reached the peak at the 5th day after injury. The expression of SDF-1α protein and mRNA maintained a high level at the 14th day after injury. Conclusion The expression of SDF-1α protein and mRNA is up-regulated after partial optic nerve injury, and may last for a long time.

Objective:To investigate the predictive value of systemic immune inflammation index (SII) for the overall survival of patients with pancreatic cancer by propensity score matching analysis.Methods:The clinical data of 457 patients with pancreatic cancer admitted to the Affiliated Hospital of Qingdao University from August 2000 to December 2019 were retrospectively analyzed. The age, gender, presence of jaundice, pancreatitis and diabetes, serum CA19-9, total bilirubin level, neutrophil count, platelet count, lymphocyte count in blood, presence of radical surgery, tumor TNM stage, tumor location and the like were recorded. The cut-off value of SII was determined by Youden index. The patients were divided into high and low SII groups accroding to the cut-off value. The propensity score matching was applied to reduce the selection bias of patients. Patients were 1∶2 matched and the caliper value was 0.1. The difference on overall survival between the two groups was compared. The prognostic factors were analyzed by univariate and multivariate Cox regression analysis. Kaplan-Meier was used to draw the overall survival curve to calculate the cumulative survival rate, and the differences between the curves were analyzed by Log-Rank test.Results:The cut-off value of SII was 765. There were statistically significant differences between the high SII group ( n=125) and the low SII group ( n=332) on the presence or absence of pancreatitis, the level of total bilirubin in blood, radical surgery, and TNM stage before the propensity score matching (all P value <0.05). After propensity score matching, there was no statistically significant difference between the high SII group ( n=113) and the low SII group ( n=182) on all the clinical parameters mentioned above except for CA19-9, indicating that the two groups were comparable. Univariate analysis showed that the level of CA19-9, SII, radical surgery and different TNM stage were all related to the overall survival of pancreatic cancer patients. Multivariate analysis showed that high CA19-9 level, high SII, no radical surgery, and worse TNM stage were independent risk factors for short overall survival, and high SII ( HR=1.882, 95% CI 1.446-2.450, P<0.001) was significantly associated with poor prognosis. The overall survival of patients with high SII was obviously shorter than the low SII group ( P<0.001), and the average survival time of patients with high and low SII were 8.86 and 11.38 months, respectively. Conclusions:SII is of great value in evaluating the overall survival of pancreatic cancer patients. Higher SII is associated with shorter overall survival.

OBJECTIVE： To establish the method for content determination of ligustilide and to optimize the extraction technology of volatile oil and inclusion technology in Quhan zhufeng granules. METHODS： HPLC method was adopted. The determination was performed on Waters C18 column with mobile phase consisted of methanol-water （70 ∶ 30， V/V） at the flow rate of 1 mL/min. The detection wavelength was set at 327 nm， and the column temperature was 30 ℃. The sample size was 10 μL. Using yield of volatile oil and the content of ligustilide as index， with soaking time， the amount of adding water and extraction time as factors， the extraction technology was optimized by orthogonal test. Using inclusion rate， the yield of inclusion compound and yield of volatile oil as index， with ratio of volatile oil to β-cyclodextrin， inclusion temperature and inclusion time as factors， the inclusion technology of volatile oil was optimized by orthogonal test. RESULTS： The linear range of ligustilide was 0.4-4 μg（r＝0.999 9）； RSDs of precision， stability and reproducibility tests were all lower than 2％ （n＝6）. The recoveries were 96.75％-102.03％（RSD＝2.06％，n＝6）. The optimal extraction technology of volatile oil included 10-fold water （mL/g）， soaking for 15 min， extracting for 8 h. Average yield of volatile oil was 0.310 7％， and average content of ligustilide was 0.418 0 mg/g. The optimal inclusion technology of volatile oil included ratio of β-cyclodextrin and volatile oil was 1 ∶ 8 （mL/g）； inclusion temperature was 50 ℃； inclusion time was 3 h. Average inclusion rate was 69.43％， and the yield of inclusion compound was 58.89％； the yield of volatile oil was 14.15％. CONCLUSIONS： Established determination method is simple， accurate and stable. The optimal extraction technology of volatile oil and inclusion technology are stable and feasible.