To verify the reliability of targeted detecting HER2 positive cancer cells and clinical pathological tissue specimens with a recombinant anti HER2 single chain antibody in single chain Fv fragment (scFv) format, we have constructed the fusion variable regions of the ScFv specific for HER2/neu. labeled a green-fluorescent protein(GFP). The humanized recombinant Anti HER2 ScFv-GFP gene was inserted into pFast Bac HT A, and expressed in insect cells sf9. Then the recombinant fusion protein Anti HER2 ScFv-GFP was properly purified with Ni2+-NTA affinity chromatography from the infected sf9 cells used to test the specificity of the fusion antibody for HER2 positive cancer cells. Firstly, the purified antibody incubated with HER2 positive breast cancer cells SKBR3, BT474 and HER2 negative breast cancer cells MCF7 for 12 h/24 h/48 h at 37 degrees C, in order to confirm targeted detecting HER2 positive breast cancer cells by Laser Confocal Microscopy. Furthermore, the same clinical pathological tissue samples were assessed by immunohistochemistry (IHC) and the fusion antibody Anti HER2 ScFv-GFP in the meanwhile. The data obtained indicated that the recombinant eukaryotic expression plasmid pFast Bac HT A/Anti HER2 ScFv-GFP was constructed successfully In addition, obvious green fluorescent was observed in insect cells sf9. When the purified fusion antibody was incubated with different cancer cells, much more green fluorescent was observed on the surface of the HER2 positive cancer cells SKBR3 and BT474. In contrast, no green fluorescent on the surface of the HER2 negative cancer cells MCF7 was detected. The concentration of the purified fusion antibody was 115.5 microg/mL, of which protein relative molecular weight was 60 kDa. The analysis showed the purity was about 97% and the titer was about 1:64. The detection results of IHC and fusion antibody testing indicated the conformity. In summary, the study showed that the new fusion antibody Anti HER2 ScFv-GFP can test HER2 positive cancer cells, indicating a potential candidate method for clinical HER2 positive specimens detection.
Animals ; Breast Neoplasms ; diagnosis ; pathology ; Female ; Genetic Vectors ; genetics ; Green Fluorescent Proteins ; genetics ; Humans ; MCF-7 Cells ; Receptor, ErbB-2 ; analysis ; Recombinant Fusion Proteins ; genetics ; Sf9 Cells ; Single-Chain Antibodies ; genetics

Objective To propose a lung artery segmentation method that integrates shape and position prior knowledge, aiming to solve the issues of inaccurate segmentation caused by the high similarity and small size differences between the lung arteries and surrounding tissues in CT images. Methods Based on the three-dimensional U-Net network architecture and relying on the PARSE 2022 database image data, shape and position prior knowledge was introduced to design feature extraction and fusion strategies to enhance the ability of lung artery segmentation. The data of the patients were divided into three groups: a training set, a validation set, and a test set. The performance metrics for evaluating the model included Dice Similarity Coefficient (DSC), sensitivity, accuracy, and Hausdorff distance (HD95). Results The study included lung artery imaging data from 203 patients, including 100 patients in the training set, 30 patients in the validation set, and 73 patients in the test set. Through the backbone network, a rough segmentation of the lung arteries was performed to obtain a complete vascular structure; the branch network integrating shape and position information was used to extract features of small pulmonary arteries, reducing interference from the pulmonary artery trunk and left and right pulmonary arteries. Experimental results showed that the segmentation model based on shape and position prior knowledge had a higher DSC (82.81%±3.20% vs. 80.47%±3.17% vs. 80.36%±3.43%), sensitivity (85.30%±8.04% vs. 80.95%±6.89% vs. 82.82%±7.29%), and accuracy (81.63%±7.53% vs. 81.19%±8.35% vs. 79.36%±8.98%) compared to traditional three-dimensional U-Net and V-Net methods. HD95 could reach (9.52±4.29) mm, which was 6.05 mm shorter than traditional methods, showing excellent performance in segmentation boundaries. Conclusion The lung artery segmentation method based on shape and position prior knowledge can achieve precise segmentation of lung artery vessels and has potential application value in tasks such as bronchoscopy or percutaneous puncture surgery navigation.