Radiation-induced optic neuropathy (RION) is a devastating complication following external beam radiation therapy (EBRT) that leads to acute vision loss. To date, no efficient, available treatment for this complication, due partly to the lack of understanding regarding the developmental processes behind RION. Here, we report radiation caused changes in mitochondrial dynamics by regulating the mitochondrial fission proteins dynamin-related protein 1 (Drp1) and fission-1 (Fis1). Concurrent with an excessive production of reactive oxygen species (ROS), both neuronal injury and visual dysfunction resulted. Further, our findings delineate an important mechanism by which cyclin-dependent kinase 5 (Cdk5)-mediated phosphorylation of Drp1 (Ser616) regulates defects in mitochondrial dynamics associated with neuronal injury in the development of RION. Both the pharmacological inhibition of Cdk5 by roscovitine and the inhibition of Drp1 by mdivi-1 inhibited mitochondrial fission and the production of ROS associated with radiation-induced neuronal loss. Taken together, these findings may have clinical significance in preventing the development of RION.

Objective. The purpose of this study was to investigate the feasibility of applying handcrafted radiomics (HCR) and deep learning-based radiomics (DLR) for the accurate preoperative classification of glioblastoma (GBM) and solitary brain metastasis (BM). Methods. A retrospective analysis of the magnetic resonance imaging (MRI) data of 140 patients (110 in the training dataset and 30 in the test dataset) with GBM and 128 patients (98 in the training dataset and 30 in the test dataset) with BM confirmed by surgical pathology was performed. The regions of interest (ROIs) on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI (T1CE) were drawn manually, and then, HCR and DLR analyses were performed. On this basis, different machine learning algorithms were implemented and compared to find the optimal modeling method. The final classifiers were identified and validated for different MRI modalities using HCR features and HCR + DLR features. By analyzing the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the predictive efficacy of different methods. Results. In multiclassifier modeling, random forest modeling showed the best distinguishing performance among all MRI modalities. HCR models already showed good results for distinguishing between the two types of brain tumors in the test dataset (T1WI, AUC = 0.86; T2WI, AUC = 0.76; T1CE, AUC = 0.93). By adding DLR features, all AUCs showed significant improvement (T1WI, AUC = 0.87; T2WI, AUC = 0.80; T1CE, AUC = 0.97; $p<0.05$ ). The T1CE-based radiomic model showed the best classification performance (AUC = 0.99 in the training dataset and AUC = 0.97 in the test dataset), surpassing the other MRI modalities ( $p<0.05$ ). The multimodality radiomic model also showed robust performance (AUC = 1 in the training dataset and AUC = 0.84 in the test dataset). Conclusion. Machine learning models using MRI radiomic features can help distinguish GBM from BM effectively, especially the combination of HCR and DLR features.

Background: Gliomablastoma multiforme (GBM) is the most fatal form of all brain cancers in human with no successful treatment available. Programmed death-ligand 1 (PD-L1) is a coinhibitory ligand predominantly expressed by tumor cells. Growth differentiation factors (GDFs) are a subfamily of proteins belonging to the transforming growth factor beta superfamily that have functions predominantly in tissue development and cancer. Purpose: To investigat the expression of GDFs in GBMs, and explored the potential regulatory role of GDFs on PD-L1 expression in GBMs. Methods: GEO2R program were analyzed for the mRNA expression data of GDFs in GSE4290 dataset. Analysis of TCGA GBM datasets were further determined the relationship between GDFs and PD-L1. Western blot Western blot was used to detect the expression of PD-L1 in GBM cell lines. Results: GDFs displayed differential patterns of expression with GDF15 and myostatin (MSTN) highly enriched in GBM tissues. We also identified GDF15 as a novel regulator that induces PD-L1 expression in GBM cells. Consistently, GDF15 expression correlated with PD-L1 in TCGA GBM dataset. Further, GDF15 enhanced PD-L1 expression via Smad2/3 pathway in GBM cell line U87, U251 and SHG44, which was inhibited by Smad2/3 inhibitor SIS3. Knockdown of GDF15 attenuated Smad2/3 signaling and reduced PD-L1 expression in A172 and GIC6 glioma cells. Conclusion: GDF15 might be a novel regulator of PD-L1 expression in GBMs; targeting GDF15/PD-L1 pathway might be a promising therapeutic approach for GBM patients.

Brain metastases (BM) is common in non-small-cell lung cancer (NSCLC) patients. Immune checkpoint inhibitors (ICIs) have gradually become a routine treatment for NSCLC BM patients. Currently, three...

Background: To evaluate the diagnostic value of Epstein–Barr virus (EBV) DNA in nasopharyngeal carcinoma (NPC) patients with locoregional or distant recurrence. Methods: Articles related to the diagnosis of recurrent or metastatic NPC by the detection of EBV DNA in plasma or serum were retrieved from different databases. Sensitivity, specificity, summary receiver operating characteristic (SROC) curves, and likelihood ratios were pooled to assess the diagnostic value of individual diagnostic tests. Results: This meta-analysis pooled 25 eligible studies including 2496 patients with NPC. The sensitivity, specificity, positive likelihood ratio (+LR), and negative likelihood ratio (−LR) of EBV DNA in the diagnosis of NPC were 0.858 (95% confidence interval (CI): 0.801–0.901), 0.890 (95% CI: 0.866–0.909), 7.782 (95% CI: 6.423–9.429) and 0.159 (95% CI: 0.112–0.226), respectively. The diagnostic odds ratio (DOR) was 48.865 (95% CI: 31.903–74.845). The SROC for EBV DNA detection was 0.93 (95% CI: 0.90–0.95). Conclusion: The detection of EBV DNA for the diagnosis of recurrent or metastatic NPC has good sensitivity and specificity and might be helpful in monitoring recurrent or metastatic NPC.