In order to improve the processing efficiency of T cell tumor antigen epitopes, this bioinformatic study compares proteolytic sites in the generation of 47 experimentally identified HLA-A2.1-restricted immunodominant tumor antigen epitopes to those of 52 documented HLA-A2.1-restricted immunodominant viral antigen epitopes. Our results show that the amino acid frequencies in the Cterminal cleavage sites of the tumor antigen epitopes, as well as several positions within the 10 amino acid (aa) flanking regions, are significantly different from those of the viral antigen epitopes. In the 9 amino acid epitope region, frequencies differed somewhat in the secondary-anchored amino acid residues on E3 (the third aa of the epitope), E4, E6, E7 and E8; however, frequencies in the primaryanchored positions, on E2 and E9, for binding in the HLA-A2.1 groove, remained almost identical. The most frequently occurring amino acid pairs in both N-terminal and C-terminal cleavage sites in the generation of tumor antigen epitopes were different from those of the viral antigen epitopes. Our findings demonstrate for the first time that these two groups of epitopes may be cleaved by distinct sets of proteasomes and peptidases or similar enzymes with lower efficiencies for tumor epitopes. In the future, in order to more effectively generate tumor antigen epitopes, targeted activation of the immunoproteasomes and peptidases that mediate the cleavage of viral epitopes could be achieved, thus enhancing our potential for antigen-specific tumor immunotherapy.Vaccines capable of eliciting T cell immune responses have been successfully developed for the prevention of 26 viral and bacterial infectious diseases (I). In contrast, despite significant progress (2), effective vaccines for most types of tumor are still lacking (3). Since most tumor antigens reported are non-mutated self-antigens (2), peripheral T cell repertoire may be tolerized to self-antigens via thymic negative selection of autoreactive T cells but reacted to viral (foreign) antigens. This model of self-tolerance via thymic selection is often considered as a mechanism underlying the efficiency differences between the vaccines against viral infections and that against tumors(4). However, 0394-6320 (2006) Copyright © by BIOLIFE, s.a.s. This publication and/or article is for individual use only and may not be further reproduced without written permission from the copyright holder. Unauthorized reproduction may result in financial and other penalties 854 X.F. YANG ET AL.self-tolerance, based on the avidity of T cells for self-MHC (major histocompatibility complex) /selfpeptide complexes in the thymic selection process, is far from absolute (4). T cells with low avidity for ubiquitously expressed self-antigens or low level expressed self-antigens can escape clonal deletion in thymus and enter the periphery (4). Thus, thymic tolerance is one of the important factors, but not the only factor in determining T cell immune responses to tumors and viral infection. T cell responses are a...

Endoscopic evaluation is the key to the management of ulcerative colitis (UC). However, there is interobserver variability in interpreting endoscopic images among gastroenterologists. Furthermore, it is time‐consuming. Convolutional neural networks (CNNs) can help overcome these obstacles and has yielded preliminary positive results. We aimed to develop a new CNN‐based algorithm to improve the performance for evaluation tasks of endoscopic images in patients with UC. A total of 12,163 endoscopic images from 308 patients with UC were collected from January 2014 to December 2021. The training set and test set images were randomly divided into 37,515 and 3191 after excluding possible interference and data augmentation. Mayo Endoscopic Subscores (MES) were predicted by different CNN‐based models with different loss functions. Their performances were evaluated by several metrics. After comparing the results of different CNN‐based models with different loss functions, High‐Resolution Network with Class‐Balanced Loss achieved the best performances in all MES classification subtasks. It was especially great at determining endoscopic remission in UC, which achieved a high accuracy of 95.07% and good performances in other evaluation metrics with sensitivity 92.87%, specificity 95.41%, kappa coefficient 0.8836, positive predictive value 93.44%, negative predictive value 95.00% and area value under the receiver operating characteristic curve 0.9834, respectively. In conclusion, we proposed a new CNN‐based algorithm, Class‐Balanced High‐Resolution Network (CB‐HRNet), to evaluate endoscopic activity of UC with excellent performance. Besides, we made an open‐source dataset and it can be a new benchmark in the task of MES classification.

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