Challenges and innovations in CAR-T cell therapy: a comprehensive analysis.

Recent years have seen a marked increase in research on chimeric antigen receptor T (CAR-T) cells, with specific relevance to the treatment of hematological malignancies. Here, the structural principles, iterative processes, and target selection of CAR-T cells for therapeutic applications are described in detail, as well as the challenges faced in the treatment of solid tumors and hematological malignancies. These challenges include insufficient infiltration of cells, off-target effects, cytokine release syndrome, and tumor lysis syndrome. In addition, directions in the iterative development of CAR-T cell therapy are discussed, including modifications of CAR-T cell structures, improvements in specificity using multi-targets and novel targets, the use of Boolean logic gates to minimize off-target effects and control toxicity, and the adoption of additional protection mechanisms to improve the durability of CAR-T cell treatment. This review provides ideas and strategies for the development of CAR-T cell therapy through an in-depth exploration of the underlying mechanisms of action of CAR-T cells and their potential for innovative modification.

Borneol promotes autophagic degradation of HIF-1α and enhances chemotherapy sensitivity in malignant glioma.

Background: Gliomas are characterized by high mortality rates and resistance. Even with conventional chemotherapy the prognosis of glioblastoma remains poor. Many medications are not optimally effective due to limited bioavailability. The bioavailability of medicine can be enhanced by borneol, a monoterpenoid substance. In this study, we investigated the effect of borneol, a commonly used Chinese medicine, on chemosensitivity in C6 glioma and U251 human glioma cell lines and elucidated its therapeutic molecular targets. Methods: The chemosensitivity-inducing effects of borneol in C6 and U251 cells were examined using CCK8 and clonal formation assays. The mechanism underlying the effect of borneol was evaluated through immunohistochemistry and western blotting assays. Further, the number of autophagosomes was determined via transmission electron microscopy. Finally, the chemical sensitization effect of borneol was evaluated in SD rats after C6 orthotopic tumor transplantation. Results: Borneol increased cytotoxicity in C6 and U251 cells in response to temozolomide (TMZ). In addition, through transmission electron microscopy, western blotting, and immunohistochemical tests, we found that borneol combined with TMZ significantly increased the level of autophagy and that hypoxia inducible factor-1(HIF-1α) is a candidate target through which borneol enhances the cytotoxic effect of TMZ. Borneol's ability to enhance HIF-1α degradation was counteracted following the administration of autophagy inhibitors. In vivo, borneol treatment was found to enhance the anticancer effect of TMZ and delay tumor progression, and this effect was closely related to its ability to promote the autophagic degradation of HIF-1α. Conclusions: HIF-1α might be a valid therapeutic target of borneol, which can be potentially applied as a chemosensitizing drug used for glioma treatment.

Cluster-Based Ensemble Learning Model for Aortic Dissection Screening.

Aortic dissection (AD) is a rare and high-risk cardiovascular disease with high mortality. Due to its complex and changeable clinical manifestations, it is easily missed or misdiagnosed. In this paper, we proposed an ensemble learning model based on clustering: Cluster Random under-sampling Smote-Tomek Bagging (CRST-Bagging) to help clinicians screen for AD patients in the early phase to save their lives. In this model, we propose the CRST method, which combines the advantages of Kmeans++ and the Smote-Tomek sampling method, to overcome an extremely imbalanced AD dataset. Then we used the Bagging algorithm to predict the AD patients. We collected AD patients' and other cardiovascular patients' routine examination data from Xiangya Hospital to build the AD dataset. The effectiveness of the CRST method in resampling was verified by experiments on the original AD dataset. Our model was compared with RUSBoost and SMOTEBagging on the original dataset and a test dataset. The results show that our model performed better. On the test dataset, our model's precision and recall rates were 83.6% and 80.7%, respectively. Our model's F1-score was 82.1%, which is 4.8% and 1.6% higher than that of RUSBoost and SMOTEBagging, which demonstrates our model's effectiveness in AD screening.