Phosphorylation of eukaryotic initiation factor 2-alpha inhibits cisplatin-mediated apoptosis of hepatocellular carcinoma cells / 中华肝脏病杂志

<p><b>OBJECTIVE</b>To investigate whether the phosphorylation (functionally inhibitive) of eukaryotic initiation factor 2-alpha (eIF2-a) affects the molecular mechanism of cisplatin-induced cellular apoptosis in human hepatocellular carcinoma (HCC).</p><p><b>METHODS</b>The human HCC cultured cell lines SMMC-7221 and HepG2 were treated with cisplatin alone (controls; 24 h) or in combination with pre-transfection of a dominant-negative eIF2-a mutant (eIF2aS51A) or pre-exposure to an eIF2-a-specific phosphatase inhibitor (salubrinal) to decrease or increase the phosphorylation level, respectively. Changes in expression of apoptosis markers were quantitatively and qualitatively assessed by flow cytometry and western blot analysis. The significance of differences among groups was assessed by analysis of variance testing and of differences between groups was assessed by t-test.</p><p><b>RESULTS</b>Cisplatin treatment induced the appropriate functional-inhibitive phosphorylation of eIF2-a on serine 51. Cisplatin treatment (10 mg/ml) induced significant apoptosis in the eIF2aS51A pre-transfected SMMC-7721 (control: 21.7 +/- 1.5% vs. 50.7 +/- 2.1%, t = 19.454, P less than 0.05) and HepG2 (21.0 +/- 1.0% vs. 57.3 +/- 2.1%, t = 27.250, P less than 0.05). Salubrinal pre-treatment significantly inhibited the cisplatin (15 mg/ml)-induced apoptosis in SMMC-7721 (control: 50.3 +/- 2.5% vs. 16.3 +/- 2.1%, t = 18.031, P less than 0.05) and HepG2 (42.0 +/- 2.6% vs. 12.0 +/- 2.0%, t = 15.667, P less than 0.05).</p><p><b>CONCLUSION</b>Phosphorylation of eIF2-a may act to inhibit cisplatin-induced apoptosis of HCC.</p>

Distribution of cerebral vascular hemodynamic indexes / 中华流行病学杂志

<p><b>OBJECTIVE</b>To describe the distribution of cerebral vascular hemodynamic indexes (CVHI).</p><p><b>METHODS</b>A number of 25,355 age 35 and over were selected in the Northeast China by cluster sampling. CVHI were checked during baseline survey and were followed to see the occurrence of stroke. Distribution of CVHI among non-stroke population, individuals prior to the onset of stroke and patients with stroke were described.</p><p><b>RESULTS</b>The CVHI accumulative score, V(mean), V(max) and V(min) were dramatically decreasing, but RV, Zcv, WV and DR were significantly increasing as age increased. V(max), RV and CP were significantly higher in males but WV was lower than that of females. The CVHI accumulative score, V(min) and RV were 95.0, 10.23 and 75.8 in non-stroke population, 51.25, 6.71 and 122.72 pre stroke group, and 55.0, 6.78 and 115.89 in patients with stroke respectively. There were significant differences among three groups after controlling of age and sex (P < 0.01).</p><p><b>CONCLUSION</b>Variance of CVHI was closely related to age, and there appeared a significant abnormal of CVHI before and after stroke.</p>

A cohort study on cerebral vascular hemodynamics accumulative score and risks of stroke / 中华流行病学杂志

<p><b>OBJECTIVE</b>To estimate relative risk (RR) of cerebral vascular hemodynamics indexes (CVHI) accumulative score as well as some other risk factors of stroke.</p><p><b>METHODS</b>A cohort study was carried out in 25 355 participants aged 35 and over in Northeast of China from 1994 to 2001. In the baseline survey, exposure rates of hypertension, diabetes, family history of stroke and hypertension, etc. were collected and CVHI was examined. CVHI accumulative score was synthesized according to contribution of single index. During the 7-year following up, 228 stroke cases were identified and stroke coming on was defined as the endpoint of observation. Uni-variable and multiple variables analysis were carried out for estimating RR of CVHI and other risk factors of stroke.</p><p><b>RESULTS</b>Uni-variables analysis indicated that RR (95% confidence interval, CI) of Hypertension, heart diseases, diabetes, family history of hypertension and stroke, overweight or obesity, alcohol intake, cigarette smoking and CVHI < 75 scores were 3.23 (2.48 - 4.20), 2.53 (1.92 - 3.33), 2.38 (1.55 - 3.64), 1.32 (1.02 - 1.72), 1.82 (1.37 - 2.41), 1.62 (1.25 - 2.11), 1.48 (1.07 - 2.04), 1.76 (1.34 - 2.31) and 7.30 (5.43 - 9.80) respectively. Cox regression analysis showed that CVHI below 75 points, family history of stroke, cigarette smoking, heart disease, hypertension, age and sex were the factors being selected in equation. There was a significant dosage response between the decrease of CVHI score and the increase of stroke risk. RR of stroke reached 12.55 when CVHI below 75 points and history of hypertension appeared in the same individuals.</p><p><b>CONCLUSION</b>Abnormality of CVHI score seemed to be the most important and independent factor among stroke risk factors. Decrease of CVHI score might serve as a marker of high risk and play important role in stroke, especially coexistence with hypertension.</p>

Principal component analysis and integral methods of cerebral vascular hemodynamic parameters / 中华流行病学杂志

<p><b>OBJECTIVE</b>To establish a predicting model for stroke according to cerebral vascular hemodynamic indexes and major risk factors of stroke.</p><p><b>METHODS</b>Participants selected from a stroke cohort with 25,355 population in China. The first step was to carry out principal component analysis using CVHI. Logistic regression with principal component and main risk factors of stroke were then served as independent variables and stroke come on as dependent variables. The predictive model was established according to coefficient of regression and probability of each participant was also estimated. Finally, ROC curve was protracted and predictive efficacy was measured.</p><p><b>RESULTS</b>The accumulative contribution rates of four principal components were 58.1%, 79.4%, 88.4% and 94.6% respectively. Seven variables were being selected into the equation with the first to fourth principal component as history of hypertension, age and sex. Area under ROC curve was 0.855 and optimal cut-off point was probability over 0.05. Sensitivity, specificity and accuracy of stroke prediction were 80.7%, 78.5% and 78.5% respectively.</p><p><b>CONCLUSION</b>The model established by principal component and regression could effectively predict the incidence of stroke coming on.</p>