Synthetic Cells and Molecules in Cellular Immunotherapy.

Cellular immunotherapy has emerged as an exciting strategy for cancer treatment, as it aims to enhance the body's immune response to tumor cells by engineering immune cells and designing synthetic molecules from scratch. Because of the cytotoxic nature, abundance in peripheral blood, and maturation of genetic engineering techniques, T cells have become the most commonly engineered immune cells to date. Represented by chimeric antigen receptor (CAR)-T therapy, T cell-based immunotherapy has revolutionized the clinical treatment of hematological malignancies. However, serious side effects and limited efficacy in solid tumors have hindered the clinical application of cellular immunotherapy. To address these limitations, various innovative strategies regarding synthetic cells and molecules have been developed. On one hand, some cytotoxic immune cells other than T cells have been engineered to explore the potential of targeted elimination of tumor cells, while some adjuvant cells have also been engineered to enhance the therapeutic effect. On the other hand, diverse synthetic cellular components and molecules are added to engineered immune cells to regulate their functions, promoting cytotoxic activity and restricting side effects. Moreover, novel bioactive materials such as hydrogels facilitating the delivery of therapeutic immune cells have also been applied to improve the efficacy of cellular immunotherapy. This review summarizes the innovative strategies of synthetic cells and molecules currently available in cellular immunotherapies, discusses the limitations, and provides insights into the next generation of cellular immunotherapies.

Point and interval forecasting for carbon trading price: a case of 8 carbon trading markets in China.

Carbon trading price (CTP) prediction accuracy is critical for both market participants and policymakers. As things stand, most previous studies have only focused on one or a few carbon trading markets, implying that the models' universality is insufficient to be validated. By employing a case study of all carbon trading markets in China, this study proposes a hybrid point and interval CTP forecasting model. First, the Pearson correlation method is used to identify the key influencing factors of CTP. The original CTP data is then decomposed into multiple series using complete ensemble empirical mode decomposition with adaptive noise. Following that, the sample entropy method is used to reconstruct the series to reduce computational time and avoid overdecomposition. Following that, a long short-term memory method optimized by the Adam algorithm is established to achieve the point forecasting of CTP. Finally, the kernel density estimation method is used to predict CTP intervals. On the one hand, the results demonstrate the proposed model's validity and superiority. The interval prediction model, on the other hand, reflects the uncertainty of market participants' behavior, which is more practical in the operation of carbon trading markets.