Inhibition of ceramide synthesis reverses endothelial dysfunction and atherosclerosis in streptozotocin-induced diabetic rats

OBJECTIVES

To explore the effect of myriocin on EDVD and atherosclerosis in diabetic rats.

METHODS

Rats were fed with a high-fat/high-sucrose/high-cholesterol diet (20% sucrose, 10% animal oil, 1.0% bile salt and 2.5% cholesterol) (hereinafter defined as diabetic groups) or Purina Rodent Chow (NC group), the former was intervened with low dose streptozotocin (30 mg/kg) after feeding 1 month to make diabetic model. The NC group was intervened with citrate buffer and the diabetic rats were intervened with myriocin (0.3 mg/kg Qod) (MTD group) or just solvent (DC group) for 14 weeks. The EDVD, thickness of fatty deposition under endothelium, ceramide, PI3K/PKB/eNOS, NO and other vital parameters were measured after the rats sacrificed.

RESULTS

In DC group, the ceramide contents in serum and aorta increased, the EDVD was impaired, the fatty deposition under endothelium increased, and the phosphorylation of PI3K/PKB/eNOS and NO release decreased all compared with the NC group (P<0.05). Compared with the DC group, the ceramide contents in MTD group decreased, the EDVD ameliorated, the fatty deposition diminished, and PI3K/PKB/eNOS phosphorylation and NO release (P<0.05) increased.

CONCLUSIONS

After treated with myriocin, the EDVD in diabetic rats has been improved by increasing PI3K/PKB/eNOS phosphorylation and NO release, and meanwhile the atherosclerosis has reversed.

Apparent Diffusion Coefficient to Evaluate Adult Intracranial Ependymomas: Relationship to Ki-67 Proliferation Index

BACKGROUND AND PURPOSE

There are important differences in the treatment and prognosis of adult intracranial low-grade ependymomas (grade II) versus anaplastic ependymomas (grade III). We evaluated the value of the apparent diffusion coefficient (ADC) for differentiating these two tumors and further investigated the relationship between ADC values and the Ki-67 proliferation index.

METHODS

Clinical and preoperative magnetic resonance imaging data of 35 cases of adult intracranial ependymomas were retrospectively analyzed, including 20 low-grade ependymomas and 15 anaplastic ependymomas. The minimum ADC (ADCmin), average ADC (ADCmean), and normalized ADC (nADC) were compared between the two groups. Receiver operating characteristic curves were drawn to evaluate the differentiating accuracy of ADC values. The Ki-67 proliferation index of the solid tumor components was also measured to explore its relationship with ADC values.

RESULTS

The ADCmin (.89 ± .17 vs. .66 ± .09 × 10^-3  mm² /second), ADCmean (.98 ± .21 vs. .72 ± .11 × 10^-3 mm² /second), and nADC (1.38 ± .31 vs. 1.02 ± .18 × 10^-3  mm² /second) were significantly higher in adult intracranial low-grade ependymomas than anaplastic ependymomas cases (all P < .05). ADCmean best distinguished the two groups, with an area under the curve value of .900. Using .716 × 10^-3  mm² /second as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the two groups were 66.7%, 100%, 85.7%, 100%, and 80%, respectively. ADCmin (r = -.490), ADCmean (r = -.449), and nADC (r = -.425) showed significant negative correlations with the Ki-67 proliferation index (all P < .05).

CONCLUSIONS

ADC values can differentiate adult intracranial low-grade ependymomas and anaplastic ependymomas, which could improve the preoperative diagnostic accuracy of these two tumors and guide their treatment.

Differentiating microcystic meningioma from atypical meningioma using diffusion-weighted imaging

PURPOSE

Microcystic meningioma (MCM) appears similar to atypical meningioma(AM) as per conventional diagnostic imaging. However, considering their different recurrence rate and prognosis, accurate differential diagnosis is essential for determine the appropriate treatment strategy. The aim of the study was to differentiate MCM from AM by diffusion-weighted imaging (DWI), in order to provide the basis for accurate preoperative diagnosis.

METHODS

The preoperative clinical data, conventional MRI and DWI data of 15 MCM and 30 AM cases were retrospectively analyzed. The average apparent diffusion coefficient (ADC_mean), minimum ADC (ADC_min) and normalized ADC (nADC) between MCM and AM were compared using two sample t-tests. The value of ADC_mean, ADC_min and nADC in the differential diagnosis of MCM and AM were calculated by the receiver operating curve (ROC) analysis.

RESULTS

The ADC_mean (1.06 ± 0.10 vs 0.80 ± 0.11 × 10^-3 mm²/s; P < 0.001), ADC_min (0.99 ± 0.10 vs 0.74 ± 0.12 × 10^-3 mm²/s; P < 0.001) and nADC (1.45 ± 0.17 vs 1.07 ± 0.17; P < .0001) were significantly higher in MCM compared to AM. ADC_mean of 0.91 × 10^-3 mm²/s showed an optimum area under the ROC curve of 0.967 ± 0.022, and distinguished between MCM and AM with 86.67% sensitivity, 100% specificity and 88.89% accuracy. In addition, its positive and negative predictive values were 96.29% and 77.78% respectively.

CONCLUSIONS

DWI can differentially diagnose MCM and AM, and ADC_mean is a potential quantitative tool that can improve preoperative diagnosis of both tumors.

Nomogram for predicting early recurrence in patients with high-grade gliomas

OBJECTIVE

To develop a nomogram to predict early recurrence of high-grade glioma (HGG) based on clinical pathology, genetic factors and MRI parameters.

METHODS

154 patients with HGG were classified into recurrence and non-recurrence groups based on the pathological diagnosis and RANO criteria. Clinical pathology information included age, sex, preoperative Karnofsky performance status (KPS) scores,grade, and cell proliferation index (Ki-67). Gene information included P53, IDH1, MGMT, and TERT expression status. All patients underwent baseline MRIs before treatment, including T1WI, T2WI, T1C, Flair, and DWI examinations. Tumor location, single/multiple tumors, tumor diameter, peritumoral edema, necrotic cyst, hemorrhage, average apparent diffusion coefficient(ADC) value, and minimum ADC values were evaluated. Univariate and multivariate logistic regression analyses were used to determine the predictors of early recurrence and build nomogram.

RESULTS

Univariate analysis showed that the number of tumors (OR, 0.258; 95% CI: 0.104, 0.639; P = 0.003) and peritumoral edema (OR, 0.965; 95% CI 0.942, 0.988; P = 0.003; mean in the recurrence group 22.04±17.21 mm; mean in the non-recurrence group 14.22±12.84 mm) were statistically significantly different in patients with early recurrence. Genetic factors associated with early recurrence included IDH1 (OR, 4.405; 95% CI 1.874, 10.353; P= 0.001), and MGMT (OR, 2.389; 95% CI 1.234, 4.628; P= 0.010). Multivariate logistic regression analysis revealed that the number of tumors (OR, 0.227; 95% CI 0.084, 0.616; P = 0.004), peritumoral edema (OR, 0.969; 95% CI 0.945, 0.993; P = 0.013), and IDH1 (OR, 4.200; 95% CI 1.602, 10.013; P= 0.004) were independent risk factors for early recurrence. The nomogram showed the highest net benefit when the threshold probability was less than 60%.

CONCLUSION

A nomogram prediction model can effectively aid in clinical treatment decisions for patients with newly diagnosed HGG .