Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL), a member of the TNF superfamily, induces tumor cell death via death receptors on target cells, without adverse effects on most normal cells. Its receptors are therefore an attractive target for antibody-mediated tumor therapy. Here, we report the creation of a lentivirus vector constructed by linking the heavy chain and the light chain of the antibody with a 2A/furin self-processing peptide in a single open reading frame that expresses a novel chimeric antibody (named as zaptuximab) with tumoricidal activity, which is consisted of the variable region of a mouse anti-human DR5 monoclonal antibody, AD5-10, and the constant region of human immunoglobulin G1. Lentivirus-expressed zaptuximab bound specifically to its antigen, DR5, and exhibited significant apoptosis-inducing activity in various tumor cell lines. The packaged recombinant virus lenti-HF2AL showed strong apoptosis-inducing activity in vitro. Meanwhile, inoculated subcutaneous human colon HCT116 tumor formation in nude mice were inhibited significantly. Moreover, there was a synergistic effect of mitomycin C (MMC) on the observed tumoricidal efficacy, prolonging the life span of nude mice with orthotopic human lung tumor cancers. These data suggest that lentivirus-mediated, 2A peptide-based anti-DR5 chimeric antibody expression may have clinical utility as an anticancer treatment and may represent a rational adjuvant therapy in combination with chemotherapy.

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Impact craters are the most prominent features on the surface of the Moon, Mars, and Mercury. They play an essential role in constructing lunar bases, the dating of Mars and Mercury, and the surface exploration of other celestial bodies. The traditional crater detection algorithms (CDA) are mainly based on manual interpretation which is combined with classical image processing techniques. The traditional CDAs are, however, inefficient for detecting smaller or overlapped impact craters. In this paper, we propose a Split-Attention Networks with Self-Calibrated Convolution (SCNeSt) architecture, in which the channel-wise attention with multi-path representation and self-calibrated convolutions can generate more prosperous and more discriminative feature representations. The algorithm first extracts the crater feature model under the well-known target detection R-FCN network framework. The trained models are then applied to detecting the impact craters on Mercury and Mars using the transfer learning method. In the lunar impact crater detection experiment, we managed to extract a total of 157,389 impact craters with diameters between 0.6 and 860 km. Our proposed model outperforms the ResNet, ResNeXt, ScNet, and ResNeSt models in terms of recall rate and accuracy is more efficient than that other residual network models. Without training for Mars and Mercury remote sensing data, our model can also identify craters of different scales and demonstrates outstanding robustness and transferability.

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