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OBJECTIVE:The role of Ulinastatin in neuronal injury after cardiopulmonary resuscitation has not been elucidated. We aim to evaluate the effects of Ulinastatin on inflammation, oxidation, and neuronal injury in the cerebral cortex after cardiopulmonary resuscitation.METHODS:Ventricular fibrillation was induced in 76 adult male Wistar rats for 6 min, after which cardiopulmonary resuscitation was initiated. After spontaneous circulation returned, the rats were split into two groups: the Ulinastatin 100,000 unit/kg group or the PBS-treated control group. Blood and cerebral cortex samples were obtained and compared at 2, 4, and 8 h after return of spontaneous circulation. The protein levels of tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6) were assayed using an enzyme-linked immunosorbent assay, and mRNA levels were quantified via real-time polymerase chain reaction. Myeloperoxidase and Malondialdehyde were measured by spectrophotometry. The translocation of nuclear factor-κB p65 was assayed by Western blot. The viable and apoptotic neurons were detected by Nissl and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL).RESULTS:Ulinastatin treatment decreased plasma levels of TNF-α and IL-6, expression of mRNA, and Myeloperoxidase and Malondialdehyde in the cerebral cortex. In addition, Ulinastatin attenuated the translocation of nuclear factor-κB p65 at 2, 4, and 8 hours after the return of spontaneous circulation. Ulinastatin increased the number of living neurons and decreased TUNEL-positive neuron numbers in the cortex at 72 h after the return of spontaneous circulation.CONCLUSIONS:Ulinastatin preserved neuronal survival and inhibited neuron apoptosis after the return of spontaneous circulation in Wistar rats via attenuation of the oxidative stress response and translocation of nuclear factor-κB p65 in the cortex. In addition, Ulinastatin decreased the production of TNF-α, IL-6, Myeloperoxidase, and Malondialdehyde.

ObjectiveTo investigate whether first-trimester fasting plasma glucose (FPG), blood coagulation function and lipid metabolism could predict gestational diabetes mellitus (GDM) risk.MethodsFrom October 2020 to May 2021, a total of 584 pregnant women who took prenatal care in Shanghai Jiaotong University Affiliated Sixth People's Hospital were chosen as the observation subjects. The clinical information and serum samples of all pregnant women were collected at 10–13 weeks of gestation and the blood coagulation function, fasting blood glucose and lipid profiles of the pregnant women were detected. A 75 g oral glucose tolerance test was performed up to 24–28 weeks of gestation. One hundred forty-two pregnant women with GDM and 442 pregnant women without GDM were detected. Data were expressed by x ± s or median (interquartile range) and were analyzed using student's t-test, Wilcoxon rank sum test and Logistic regression analysis. The area under the curve (AUC) was calculated by receiver operating characteristic curve (ROC) to analyze the predictive values.ResultsCompared with non-GDM group, age, pre-pregnancy BMI, FPG, FIB, D-Dimer, FDP, FPG, TC, TG, LDL-C, sdLDL-C, APOB and APOE in GDM group were significantly higher than those in non-GDM group, while PT, INR, APTT and TT were significantly lower than those in non-GDM group. Univariate logistic regression analysis was used to explore the risk factors of GDM. Gestational age, pre-pregnancy BMI, FPG, PT, INR, APTT, FIB, TT, D-Dimer, TC, TG, LDL-C, sdLDL-C, APOB and APOE were all independent predictors of GDM. Multivariatelogistic regression showed that pre-pregnancy BMI, FPG, APTT, TT, TG, LDL-C, sdLDL-C and APOB were risk factors for GDM. The AUC of the established GDM risk prediction model was 0.892 (0.858–0.927), and the sensitivity and specificity were 80.71 and 86.85%, respectively; which were greater than that of pre-pregnancy BMI, FPG, APTT, TT,TG, LDL-C, sdLDL-C, APOB alone, and the difffference was statistically signifificant (P < 0.05).ConclusionsFPG, APTT, TT, TG, LDL-C, sdLDL-C, APOB and pre-pregnancy BMI in early pregnancy has important clinical value for the prediction of GDM, We combined these laboratory indicators and established a GDM risk prediction model, which is conducive to the early identification, intervention and treatment of GDM, so as to reduce the morbidity of maternal and infant complications.