Cerebral ischemia is a leading cause of death and disability. A previous study indicated that remote ischemic postconditioning (RIP) in the treatment of cerebral ischemia reduces ischemia/reperfusion (I/R) injury. However, the underlying mechanism is not well understood. In the present study, the authors hypothesized that the protective effect of RIP on neurological damage is mediated by exosomes that are released by endothelial cells in femoral arteries. To test this, right middle cerebral artery occlusion/reperfusion with RIP was performed in rats. In addition, an I/R injury cell model was tested that included human umbilical vein endothelial cells (HUVECs) and SH-SY5Y cells. Both the in vivo and in vitro models were examined for injury. Markers of exosomes (CD63, HSP70 and TSG101) were assessed by immunohistochemistry, western blot analysis and flow cytometry. Exosomes were extracted from both animal serum and HUVEC culture medium and identified by electron microscopy. They investigated the role of endothelial cell-derived exosomes in the proliferation, apoptosis, cell cycle, migration and invasion of I/R-injured SH-SY5Y cells. In addition, apoptosis-related molecules caspase-3, Bax and Bcl-2 were detected. RIP was determined to increase the number of exosomes and the expression levels of CD63, HSP70 and TSG101 in plasma, but not in brain hippocampal tissue. The size of exosomes released after I/R in HUVECs was similar to the size of exosomes released in rats subjected to RIP. Endothelial cell-derived exosomes partly suppressed the I/R-induced cell cycle arrest and apoptosis, and inhibited cell proliferation, migration and invasion in SH-SY5Y nerve cells. Endothelial cell-derived exosomes directly protect nerve cells against I/R injury, and are responsible for the protective role of RIP in I/R.

VT-NLCs can deliver VCR and TMZ into U87MG cells more efficiently, and inhibition efficacy is higher than VT-SLNs. This dual drugs-loaded NLCs could be an outstanding drug delivery system to achieve excellent therapeutic efficiency for the treatment of malignant gliomatosis cerebri.

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Background: Prediction of radiotherapeutic response before radiotherapy could help determine individual treatment strategies for patients with acromegaly. Objective: To develop and validate a machine-learning-based multiparametric MRI radiomics model to non-invasively predict radiotherapeutic response in patients with acromegaly. Methods: This retrospective study included 57 acromegaly patients who underwent postoperative radiotherapy between January 2008 and January 2016. Manual lesion segmentation and radiomics analysis were performed on each pituitary adenoma, and 1561 radiomics features were extracted from each sequence. A radiomics signature was built with a support vector machine using leave-one-out cross-validation for feature selection. Multivariable logistic regression analysis was used to select appropriate clinicopathological features to construct a clinical model, which was then combined with the radiomics signature to construct a radiomics model. The performance of this radiomic model was assessed using receiver operating characteristics (ROC) analysis and its calibration, discriminating ability, clinical usefulness. Results: At 3-years after radiotherapy, 25 patients had achieved remission and 32 patients had not. The clinical model incorporating seven clinical features had an area under the ROC (AUC) of 0.86 for predicting radiotherapeutic response, and performed better than any single clinical feature. The radiomics signature constructed with six radiomics features had a significantly higher AUC of 0.92. The radiomics model showed good discrimination abilities and calibration, with an AUC of 0.96. Decision curve analysis confirmed the clinical utility of the radiomics model. Conclusion: Using pre-radiotherapy clinical and MRI data, we developed a radiomics model with favorable performance for individualized non-invasive prediction of radiotherapeutic response, which may help in identifying acromegaly patients who are likely to benefit from radiotherapy.