Screening and characterization of the scFv for chimeric antigen receptor T cells targeting CEA-positive carcinoma

IntroductionChimeric antigen receptor T (CAR-T) cell therapy presents a promising treatment option for various cancers, including solid tumors. Carcinoembryonic antigen (CEA) is an attractive target due to its high expression in many tumors, particularly gastrointestinal cancers, while limited expression in normal adult tissues. In our previous clinical study, we reported a 70% disease control rate with no severe side effects using a humanized CEA-targeting CAR-T cell. However, the selection of the appropriate single-chain variable fragment (scFv) significantly affects the therapeutic efficacy of CAR-T cells by defining their specific behavior towards the target antigen. Therefore, this study aimed to identify the optimal scFv and investigate its biological functions to further optimize the therapeutic potential of CAR-T cells targeting CEA-positive carcinoma.MethodsWe screened four reported humanized or fully human anti-CEA antibodies (M5A, hMN-14, BW431/26, and C2-45), and inserted them into a 3rd-generation CAR structure. We purified the scFvs and measured the affinity. We monitored CAR-T cell phenotype and scFv binding stability to CEA antigen through flow cytometry. We performed repeated CEA antigen stimulation assays to compare the proliferation potential and response of the four CAR-T cells, then further evaluated the anti-tumor efficacy of CAR-T cells ex vivo and in vivo.ResultsM5A and hMN-14 CARs displayed higher affinity and more stable CEA binding ability than BW431/26 and C2-45 CARs. During CAR-T cell production culture, hMN-14 CAR-T cells exhibit a larger proportion of memory-like T cells, while M5A CAR-T cells showed a more differentiated phenotype, suggesting a greater tonic signal of M5A scFv. M5A, hMN-14, and BW431/26 CAR-T cells exhibited effective tumor cell lysis and IFN-γ release when cocultured with CEA-positive tumor cells in vitro, correlating with the abundance of CEA expression in target cells. While C2-45 resulted in almost no tumor lysis or IFN-γ release. In a repeat CEA antigen stimulation assay, M5A showed the best cell proliferation and cytokine secretion levels. In a mouse xenograft model, M5A CAR-T cells displayed better antitumor efficacy without preconditioning.DiscussionOur findings suggest that scFvs derived from different antibodies have distinctive characteristics, and stable expression and appropriate affinity are critical for robust antitumor efficacy. This study highlights the importance of selecting an optimal scFv in CAR-T cell design for effective CEA-targeted therapy. The identified optimal scFv, M5A, could be potentially applied in future clinical trials of CAR-T cell therapy targeting CEA-positive carcinoma

Thermal Lattice Boltzmann Simulation of Evaporating Thin Liquid Film for Vapor Generation

Thin film evaporation (TFE) plays an important role in many industrial applications, such as power generation, cooling, and thermal management. Effective evaporation takes place in the thin liquid film region with relatively low film thickness and low intermolecular forces. In this paper, a numerical approach based on the thermal lattice Boltzmann method (TLBM) is employed to investigate the heat and mass transfer phenomena in TFE. The TLBM approach is validated by simulating some benchmark problems, and is then used to study a vapor generation problem where TFE is involved. Specifically, vapor is generated from evaporating pores, the solid walls of which are hydrophilic. Factors that affect the overall vapor generation efficiency are investigated via the numerical approach. Methods that can improve the overall efficiency are further proposed. Simulations reveal that distributed scenarios (using distributed small pores instead of a big one) and hydrophobic pore ends render more efficient vapor generation

Prediction of dissolved gas concentration in transformer oil considering data loss scenarios in power system

The trend prediction of dissolved gas concentration in transformer oil can provide basis for transformer fault diagnosis, which is of vital significance to the safe operation of power system. However, due to the inevitable malfunction of monitoring equipment, it is difficult to collect all needed data in actual operation scenarios. Therefore, a method for predicting dissolved gas concentration in transformer oil for data loss scenarios is proposed based on Bayesian probabilistic matrix factorization (BPMF) and gated recurrent unit (GRU) neural network. Firstly, aiming at the problem of data loss in actual monitoring of dissolved gas in oil, BPMF is used to fill in the missing data. Then, a GRU neural network model is established to predict the trend of dissolved gas concentration in oil. Finally, the hyperparameters of the prediction model are selected and optimized by Bayesian theory. The examples show that this method can effectively fill in the missing part of the measured data. Compared with traditional prediction methods, the proposed method has higher prediction accuracy

The genus Syntozyga Lower (Lepidoptera, Tortricidae) in China, with descriptions of two new species

Species of the genus Syntozyga Lower, 1901 (Lepidoptera, Tortricidae, Olethreutinae) from China are studied. Syntozyga apicispinata sp. nov. and S. similispirographa sp. nov. are described, S. pedias (Meyrick, 1920) is recorded for the first time from China, and S. spirographa (Diakonoff, 1968) is newly recorded from the Chinese mainland. Adults and genitalia are illustrated, and a distribution map of the Chinese species is given. Keys to identify the Chinese species of Syntozyga are provided. Species of the genus are well clustered in a neighbor-joining tree based on the sequence data of the COI gene. COI sequences corresponding to the new species and S. spirographa (Diakonoff, 1968) are submitted to BOLD

Coordinated optimization method for suppressing transient overvoltage caused by HVDC commutation failure considering large wind power integration

In order to suppress the sending end transient overvoltage caused by HVDC commutation failure considering large wind power integration, this paper proposes a coordinated optimization method for the control parameters of DC and wind power, which can suppress the sending end transient overvoltage and ensure the performance of the DC control system. By analyzing the mechanism of DC and wind power control parameters affecting the sending end transient overvoltage, and studying the influence of control parameters on the performance of the DC control system, a multi-objective and multi-equipment coordination optimization model of the HVDC transmission system considering large wind power integration is constructed. The multi-objective adaptive differential evolution algorithm is used to realize the coordinated optimization of DC and wind power control parameters based on PSCAD simulation joint invocation. Finally, through simulation and optimization in different cases, it is found that the multi-objective parameter optimization has more obvious advantages than single-objective optimization in the case of weak receiving end power grid. In addition, it is concluded that the same control parameters can be used in different wind power penetration scenarios to achieve the purpose of suppressing transient overvoltage, which verifies the effectiveness and accuracy of the method proposed in this paper