Acute kidney injury (AKI) is a prevalent and lethal adverse event that severely affects cancer patients receiving chemotherapy. It is correlated with the collateral damage to renal cells caused by reactive oxygen species (ROS). Currently, ROS management is a practical strategy that can reduce the risk of chemotherapy-related AKI, but at the cost of chemotherapeutic efficacy. Herein, we report catalytic activity tunable ceria nanoparticles (CNPs) that can prevent chemotherapy-induced AKI without interference with chemotherapeutic agents. Specifically, in the renal cortex, CNPs exhibit catalytic activity that decomposes hydrogen peroxide, and subsequently regulate the ROS-involved genes by activating the Nrf2/Keap1 signaling pathway. These restore the redox homeostasis for the protection of kidney tubules. Under an acidic tumor microenvironment, CNPs become inert due to the excessive H+ that disrupts the re-exposure of active catalytic sites, allowing a buildup of chemotherapy-mediated ROS generation to kill cancer cells. As ROS-modulating agents, CNPs incorporated with context-dependent catalytic activity, hold a great potential for clinical prevention and treatment of AKI in cancer patients.

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Disintegration in thalamocortical integration suggests its role in the mechanistic ‘switch’ from recreational to dysregulated drug seeking/addiction. In this study, we aimed to address whether thalamic nuclear groups show altered functional connectivity within the cerebral cortex in chronic ketamine users. One hundred and thirty subjects (41 ketamine users and 89 control subjects) underwent rsfMRI (resting-state functional Magnetic Resonance Imaging). Based on partial correlation functional connectivity analysis we partitioned the thalamus into six nuclear groups that correspond well with human histology. Then, in the area of each nuclear group, the functional connectivity differences between the chronic ketamine user group and normal control group were investigated. We found that the ketamine user group showed significantly less connectivity between the thalamic nuclear groups and the cortical regions-of-interest, including the prefrontal cortex, the motor cortex /supplementary motor area, and the posterior parietal cortex. However, no increased thalamic connectivity was observed for these regions as compared with controls. This study provides the first evidence of abnormal thalamocortical connectivity of resting state brain activity in chronic ketamine users. Further understanding of pathophysiological mechanisms of the thalamus in addiction (ketamine addiction) may facilitate the evaluation of much-needed novel pharmacological agents for improved therapy of this complex disease.

PURPOSE: This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with the conventional CT evaluation method based on subjective CT image features. RESULTS: Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. −1.514±1.800 (mean±standard deviation) with p <  0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases. CONCLUSION: A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment.