Endogenous Signaling Molecule Activating (ESMA) CARs: A Novel CAR Design Showing a Favorable Risk to Potency Ratio for the Treatment of Triple Negative Breast Cancer.

As chimeric antigen receptor (CAR) T cell therapy continues to gain attention as a valuable treatment option against different cancers, strategies to improve its potency and decrease the side effects associated with this therapy have become increasingly relevant. Herein, we report an alternative CAR design that incorporates transmembrane domains with the ability to recruit endogenous signaling molecules, eliminating the need for stimulatory signals within the CAR structure. These endogenous signaling molecule activating (ESMA) CARs triggered robust cytotoxic activity and proliferation of the T cells when directed against the triple-negative breast cancer (TNBC) cell line MDA-MB-231 while exhibiting reduced cytokine secretion and exhaustion marker expression compared to their cognate standard second generation CARs. In a NOD SCID Gamma (NSG) MDA-MB-231 xenograft mouse model, the lead candidate maintained longitudinal therapeutic efficacy and an enhanced T cell memory phenotype. Profound tumor infiltration by activated T cells repressed tumor growth, further manifesting the proliferative capacity of the ESMA CAR T cell therapy. Consequently, ESMA CAR T cells entail promising features for improved clinical outcome as a solid tumor treatment option.

An Approach to the Automatic Construction of a Road Accident Scheme Using UAV and Deep Learning Methods.

Recreating a road traffic accident scheme is a task of current importance. There are several main problems when drawing up a plan of accident: a long-term collection of all information about an accident, inaccuracies, and errors during manual data fixation. All these disadvantages affect further decision-making during a detailed analysis of an accident. The purpose of this work is to automate the entire process of operational reconstruction of an accident site to ensure high accuracy of measuring the distances of the relative location of objects on the sites. First the operator marks the area of a road accident and the UAV scans and collects data on this area. We constructed a three-dimensional scene of an accident. Then, on the three-dimensional scene, objects of interest are segmented using a deep learning model SWideRNet with Axial Attention. Based on the marked-up data and image Transformation method, a two-dimensional road accident scheme is constructed. The scheme contains the relative location of segmented objects between which the distance is calculated. We used the Intersection over Union (IoU) metric to assess the accuracy of the segmentation of the reconstructed objects. We used the Mean Absolute Error to evaluate the accuracy of automatic distance measurement. The obtained distance error values are small (0.142 ± 0.023 m), with relatively high results for the reconstructed objects' segmentation (IoU = 0.771 in average). Therefore, it makes it possible to judge the effectiveness of the proposed approach.