Objective To observe the difference of caveolin-3(CAV3) gene polymorphism between normal people and diabetic patients in Chinese Han population. Methods Exon gene polymorphism in 50 normal people and 50 T2DM patients were detected by PCR-SSCP. Results The cumulative incidence rate of electrophoretic variation in T2DM patients was 48%, while cumulative incidence rate of normal people was 7%(P<0.001). It was proved that in the variant bands, there were base variant. Conclusions The variant base number of CAV3 gene in human T2DM samples are significantly more than the normal which can be preliminary detected by PCR-SSCP. It indicates that CAV3 gene polymorphism may be one of the genetic backgrounds for the occurence of Chinese T2DM.

Numerous studies have demonstrated that estrogens may exert multifaceted effects on the cardiovascular system via activating the classical nuclear receptors ERα or ERβ and the novel G protein coupled estrogen receptor (Gper). However, some studies have reported inconsistent cardiovascular phenotypes in Gper-deficient mice. The current study was aimed to reveal the effects of genetic deletion of Gper on the arterial blood pressure (ABP) and heart rate in rats. Gper-deficient Sprague-Dawley rats were generated by utilizing the CRISPR-Cas9 gene-editing technique. ABP of 10-week old male (n = 6) and 12-week old female (n = 6) Gper-deficient rats and age-matched wild type (WT) rats (6 females and 6 males) were measured under awake and restrained conditions through the non-invasive tail-cuff method daily for 8 (females) or 9 days (males). In the male WT rats, ABP and heart rate were slightly higher in day 1 to 4 than those in day 5 to 9, indicative of stress-related sympathoexcitation in the first few days and gradual adaptation to the restrained stress in later days. Gper-deficient rats had significantly higher ABP initially (male: day 1 to day 5; female: day 1 to day 3) and similar ABP in later days of measurement compared with the WT rats. The heart rate of male Gper-deficient rats was consistently higher than that of the male WT rats from day 1 to day 8. Both male and female Gper-deficient rats appeared to show slower body weight gain than the WT counterparts during the study period. Under anesthesia, ABP of Gper-deficient rats was not significantly different from their WT counterparts. These results indicate that Gper-deficient rats may be more sensitive to stress-induced sympathoexcitation and highlight the importance of Gper in the regulation of the cardiovascular function in stressful conditions.

Objective:To investigate risk factors for vascular cognitive impairment(VCI)in elderly patients 12-18 months after the onset of acute partial anterior circulation infarction(PACI), and to establish a diagnostic and predictive model.Methods:This was a prospective study. Demographic characteristics, vascular risk factors and laboratory data of 148 patients with acute PACI were collected, and patients were followed up for 12-18 months.The Montreal Cognitive Assessment Scale(MoCA)was used to evaluate patients' cognitive function.Logistic stepwise regression was used to screen risk factors for VCI.We established a diagnostic and predictive model.The area under the receiver operating(ROC)curve(AUC)was used to evaluate the efficiency of the model.Results:A total of 126 subjects completed the 12-18 month follow-up.Multivariate logistic regression analysis found that high homocysteine(Hcy)( OR=1.082, 95% CI: 1.002-1.167), high glycated hemoglobin(HbA1c)( OR=1.653, 95% CI: 1.052-2.598), high National Institutes of Health Stroke Scale(NIHSS)score( OR=1.291, 95% CI: 1.098-1.518), high hypersensitive C-reactive protein(hs-CRP)( OR=1.026, 95% CI: 1.005-1.047)and low education level( OR=2.485, 95% CI: 1.231-5.018)were independent risk factors for VCI in patients 12-18 months after PACI( P<0.05). The AUC of the diagnostic and predictive model was 0.828(95% CI: 0.755-0.902). Conclusions:High Hcy, NIHSS score, hs-CRP and low education level are independent risk factors for VCI in patients 12-18 months after PACI.The diagnostic and predictive model can help to screen patients at high-risk for VCI, so that timely clinical recognition, diagnosis and treatment can be made after acute PACI.