Objective To observe the expression ofprobucol on high glucose-induced specificity protein 1 (SP 1),kelchlike ECH associated protein 1 (Keap 1),NF-E2-related factor 2 (Nrf2) and glutamatecysteine ligase catalytic (GCLC) in the cultured human müller cells and preliminary study the antioxidation of the probucol on müller cells.Methods Primary cultured human müller cells were randomly divided into four groups:normoglycaemia group (5.5 mmol/L glucose),normoglycaemia with probucol group (5.5 mmol/L glucose+100 μmol/L probucol),hyperglycemia group (25.0 mmol/L glucose),hyperglycemia with probucol group (25.0 mmol/L glucose + 100 μmol/L probucol).Immunofluorescence staining was used to assess distribution of SP1,Keapl,Nrf2,GCLC in human Müller cells.SP1,Keapl,Nrf2 and GCLC messenger RNA (mRNA) expression was evaluated by quantitative real-time RT-PCR (qRT-PCR).Independent sample t test was used to compare the data between the two groups.Results All müller cells expressed glutamine synthetase (＞ 95％),which confirmed the cultured cells in vitro were the purification of generations of müller cells.The expressions of SP 1,Keap 1,Nrf2,and GCLC protein were positive in human müller cells.qRT-PCR indicated that SP1 (t=28.30,P＜0.000),Keap1 (t=5.369,P=0.006),and Nrf2 (t=10.59,P=0.001) mRNA in the hyperglycemia group increased obviously compared with the normoglycaemia group;GCLC (t=4.633,P=0.010)mRNA in the hyperglycemia group decreased significantly compared with the normoglycaemia group.However,SP1 (t=12.60,P=0.000) and Keapl (t=4.076,P=0.015) in the hyperglycemia with probucol group decreased significantly compared with the hyperglycemia group;Nrf2 (t=12.90,P=0.000) and GCLC (t=l 5.96,P＜0.000)mRNA in the hyperglycemia with probucol group increased obviously compared with with the hyperglycemia group.Conclusion Probucol plays an antioxidant role by inhibiting the expression of SP 1,Keap 1 and upregulating the expression of Nrf2,GCLC in müller cells induced by high glucose.

Objective:To evaluate the effect of deep learning based on DWI and fluid attenuated inversion recovery (FLAIR) to construct a prediction model of the onset time in acute stroke.Methods:A total of 324 cases of acute stroke with clear onset time, from January 2017 to May 2020 in Nanjing First Hospital, were retrospectively enrolled and analyzed. The patients were divided into a training set of 226 patients and a test set of 98 patients according to the complete randomization method using a 7∶3 ratio, and the patients were divided into ≤ 4.5 h and >4.5 h according to symptom onset time in each group. The acute infarction areas on DWI and the corresponding high signal area on FLAIR were manually outlined by physician. Using the InceptionV3 model as the basic model for image features extraction, the deep learning prediction model based on single sequence (DWI, FLAIR) and multi sequences (DWI+FLAIR) were established and verified. Then the area under curve (AUC), accuracy of human readings, single sequence model and multi sequence model in predicting the acute stroke onset time from imaging were compared.Results:DWI-FLAIR mismatch was found in 94 cases (94/207) of patients with symptom onset time from imaging ≤ 4.5 h, while in 28 cases (28/117) of patients with symptom onset time from imaging >4.5 h. ROC analysis showed that the AUC of DWI-FLAIR mismatch in predicting acute stroke onset time from imaging was 0.607, and the accuracy was 60.2%. The prediction model of deep learning based on single sequence showed that the AUC of FLAIR was 0.761 and the accuracy was 71.4%; the AUC of DWI was 0.836 and the accuracy was 81.6%. The AUC of predicting stroke onset time based on the multi-sequence (DWI+FLAIR) deep learning model was 0.852, which was significantly better than that of manual identification ( Z = 0.617, P = 0.002), FLAIR sequence deep learning model ( Z = 2.133, P = 0.006) and DWI sequence deep learning model ( Z = 1.846, P = 0.012). Conclusion:The deep learning model based on DWI and FLAIR is superior to human readings in predicting acute stroke onset time from imaging, which could provide guidance for intravenous thrombolytic therapy for acute stroke patients with unknown onset time.