Diterpenoid DGA induces apoptosis via endoplasmic reticulum stress caused by changes in glycosphingolipid composition and inhibition of STAT3 in glioma cells.

Glioma is one of the most common malignant primary brain tumors, with poor prognosis and high recurrence. There are currently few drugs approved for brain tumors; thus, it is necessary to develop new effective drugs. Natural diterpenoids have important biological activities, including antiinflammatory, antioxidative, and antitumor effects. In this study, 7α,14ß-dihydroxy-ent-kaur-17-dimethylamino-3,15-dione (DGA), a diterpenoid compound modified from glaucocalyxin A, inhibited the proliferation of many tumor cells, especially glioma. Flow cytometry analysis showed that DGA induced apoptosis in glioma cells. DGA also inhibited xenograft tumors in nude mice. It affected the expression of ceramide synthases (CerS) in glioma cells; CerS1 decreased, and CerS2 and CerS5 increased, resulting in a change in the composition of glycosphingolipids containing varying acyl chain lengths. In glioma cells treated with DGA, the gene transcription of activating transcription factor 4 (ATF4), X-box binding protein-1 (XBP1), and C/EBP-homologous protein (CHOP) in unfolded protein response pathways was upregulated. Meanwhile, the ratio of proapoptotic protein Bcl-2-associated X protein (BAX) to antiapoptotic protein B-cell lymphoma 2 (Bcl-2) also increased. This suggested that an imbalance of glycosphingolipids caused by DGA induced severe endoplasmic reticulum stress and triggered cell apoptosis. Moreover, Western blotting showed DGA inhibited the signal transducers and activators of transcription 3 (STAT3) signaling pathway by reducing the phosphorylation of STAT3 and its upstream kinases, which also promoted the apoptosis of glioma cells. Together, these results explored the anticancer activities of DGA and highlighted it as a potential candidate for treating glioma.

A method to calculate the number of dynamic HDFS copies based on file access popularity.

HDFS heterogeneous clusters usually have multiple storage media at the same time. How to efficiently read and write file copies and reasonably use various storage media is a problem to be solved. Dynamically adjusting the number of copies is important in HDFS, which can solve the problem of accessing a large number of hot files at the same time and improve the efficiency of cluster services. A method is introduced to calculate the number of dynamic HDFS copies based on file access popularity in this paper. Firstly, an algorithm was proposed to predict file popularity based on the cuckoo search optimization Markov model. The unbiased grey model is used to predict the accessing file's popularity at the next moment according to the recent access of the file. The cuckoo search is used to optimize the Markov model, and the prediction error is corrected. Then, the calculation method of the number of copies is designed based on the prediction of the popularity of the file to be accessed and the availability of the node. The experiment shows that the proposed method has a high fitting degree with the actual value, and the MAPE is 3.08%, and it is the smallest, compared with several commonly used prediction models. In CloudSim4.0 simulation platform, multiple users write 10 files to the cluster at the same time, and the change number of copies is calculated according to the predicted value at the next moment, so as to improve the user access efficiency.