Hippocampal neuron death is a key factor in vascular dementia (VD) induced by chronic cerebral hypoperfusion (CCH). Dl-3-n-butylphthalide (NBP) is a multiple-effects drug. Therefore, the potential molecular mechanisms underlying CCH and its feasible treatment should be investigated. This study had two main purposes: first, to identify a potential biomarker in a rat model of CCH induced VD using antibody microarrays; and second, to explore the neuroprotective role of NBP at targeting the potential biomarker. Glial cell line-derived neurotrophic factor (GDNF)/GDNF family receptor alpha-1 (GFRα1)/receptor tyrosine kinase (Ret) signaling is altered in the hippocampus of CCH rats; however, NBP treatment improved cognitive function, protected against hippocampal neuron apoptosis via regulation of GDNF/GFRα1/Ret, and activated the phosphorylation AKT (p-AKT) and ERK1/2 (p-ERK1/2) signaling. We also found that 1 h oxygen-glucose deprivation (OGD) followed by 48 h reperfusion (R) in cultured hippocampal neurons led to downregulation of GDNF/GFRα1/Ret. NBP upregulated the signaling and increased neuronal survival. Ret inhibitor (NVP-AST487) inhibits Ret and downstream effectors, including p-AKT and p-ERK1/2. Additionally, both GDNF and GFRα1 expression are markedly inhibited in hippocampal neurons by coincubation with NVP-AST487, particularly under conditions of OGD/R. GDNF/GFRα1/Ret signaling and neuronal viability can be maintained by NBP, which activates p-AKT and p-ERK1/2, increases expression of Bcl-2, and decreases expression of Bax and cleaved caspase-3. The current study showed that GDNF/GFRα1/Ret signaling plays an essential role in the CCH induced VD. NBP was protective against hippocampal neuron apoptosis, and this was associated with regulation of GDNF/GFRα1/Ret and AKT/ERK1/2 signaling pathways, thus reducing cognitive impairment.
Background: Neoadjuvant chemotherapy (NAC) is commonly utilized in preoperative treatment for local breast cancer, and it gives high clinical response rates and can result in pathologic complete response (pCR) in 6-25% of patients. In recent years, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been increasingly used to assess the pathological response of breast cancer to NAC. In present analysis, we assess the diagnostic performance of DCE-MRI in evaluating the pathological response of breast cancer to NAC. Materials and Methods:A systematic search in PubMed, the Cochrane Library, and Web of Science for original studies was performed. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the methodological quality of the included studies. Patient, study, and imaging characteristics were extracted, and sufficient data to reconstruct 2 × 2 tables were obtained. Data pooling, heterogeneity testing, forest plot construction, meta-regression analysis and sensitivity analysis were performed using Stata version 12.0 (StataCorp LP, College Station, TX).Results: Eighteen studies (969 patients with breast cancer) were included in the present meta-analysis. The pooled sensitivity and specificity of DCE-MRI were 0.80 (95% confidence interval [CI]: 0.70, 0.88) and 0.84 (95% [CI]: 0.79, 0.88), respectively. Meta-regression analysis found no significant factors affecting heterogeneity. Sensitivity analysis showed that studies that set pathological complete response (pCR) (n = 14) as a responder showed a tendency for higher sensitivity compared with those that set pCR and near pCR together (n = 5) as a responder (0.83 vs. 0.72), and studies (n = 14) that used DCE-MRI to early predict the pathological response of breast cancer had a higher sensitivity (0.83 vs. 0.71) and equivalent specificity (0.80 vs. 0.86) compared to studies (n = 5) that assessed the response after NAC completion. Conclusion:Our results indicated that DCE-MRI could be considered an important auxiliary method for evaluating the pathological response of breast cancer to NAC and Cheng et al.Breast Cancer Response in DCE-MRI used as an effective method for dynamically monitoring the efficacy during NAC. DCE-MRI also performed well in predicting the pCR of breast cancer to NAC. However, due to the heterogeneity of the included studies, caution should be exercised in applying our results.
Rationale and Objectives: Controversy still exists on the diagnosability of diffusion tensor imaging (DTI) for breast lesions characterization across published studies. The clinical guideline of DTI used in the breast has not been established. This meta-analysis aims to pool relevant evidences and evaluate the diagnostic performance of DTI in the differential diagnosis of malignant and benign breast lesions.Materials and Methods: The studies that assessed the diagnostic performance of DTI parameters in the breast were searched in Embase, PubMed, and Cochrane Library between January 2010 and September 2019. Standardized mean differences and 95% confidence intervals of fractional anisotropy (FA), mean diffusivity (MD), and three diffusion eigenvalues (λ1, λ2, and λ3) were calculated using Review Manager 5.2. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated with a bivariate model. Publication bias and heterogeneity between studies were also assessed using Stata 12.0.Results: Sixteen eligible studies incorporating 1,636 patients were included. The standardized mean differences indicated that breast cancers had a significantly higher FA but lower MD, λ1, λ2, and λ3 than those of benign lesions (all P < 0.05). Subgroup analysis indicated that invasive breast carcinoma (IBC) had a significantly lower MD value than that of ductal carcinoma in situ (DCIS) (P = 0.02). λ1 showed the best diagnostic accuracy with pooled sensitivity, specificity, and AUC of 93%, 92%, and 0.97, followed by MD (AUC = 0.92, sensitivity = 87%, specificity = 83%) and FA (AUC = 0.76, sensitivity = 70%, specificity = 70%) in the differential diagnosis of breast lesions.Conclusion: DTI with multiple quantitative parameters was adequate to differentiate breast cancers from benign lesions based on their biological characteristics. MD can further distinguish IBC from DCIS. The parameters, especially λ1 and MD, should attract our attention in clinical practice.
The primary aim of the present study was to evaluate abnormal iron distribution in specific regions of the brains in patients with Parkinson's disease (PD) using quantitative susceptibility mapping (QSM) and R2 * mapping, and to compare the diagnostic performances of QSM and R2 * mapping in differentiating patients with PD with that in normal controls. A total of 25 patients with idiopathic PD and 28 sex-and age-matched normal controls were included in the present study and their brains investigated using a 3T scanner. Magnetic resonance imaging techniques, namely, QSM and R2 * mapping, were applied to generate susceptibility and R2 * values. The differences in susceptibility and R2 * values in deep grey matter nuclei between patients with PD and the normal controls were compared using independent samples t-tests. The abilities of QSM and R2 * mapping to classify patients with PD and normal controls were analyzed using receiver operating characteristic curves. Correlation analyses between imaging parameters (e.g. susceptibility and R2 * values) and clinical feature (disease severity assessed using the Hoehn and Yahr score) were performed. The intra-class correlation coefficient (ICC) for susceptibility (ICC= 0.977; P<0.001) and R2 * (ICC=0.945; P<0.001) values between two neuro-radiologists were >0.81, showing excellent inter-rater agreement. The susceptibility values were significantly increased in the substantia nigra (SN) and red nucleus, but were decreased in the putamen of patients with PD compared with that in the corresponding brain regions of normal controls. However, increased R2 * values were observed only in the SN in patients with PD. QSM showed higher sensitivity and specificity compared with R2 * mapping to separate the patients with PD from the normal controls. There were no significant correlations between the susceptibility/R2 * values and clinical features in all targeted regions of the brains in patients with PD. In conclusion, both QSM and R2 * mapping are feasible to calculate the iron levels in human brains, and QSM provides a more sensitive and accurate method to assess regional abnormal iron distribution in patients with PD.
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