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Background: It has been reported that programmed death‐ligand 1 (PD‐L1) is highly expressed in cells during viral infection, which helps the virus escape host immunity. However, the relationship between human papillomavirus (HPV) and PD‐L1 in condyloma acuminatum and whether they participate in immunosuppression have not been reported. In this paper, we aimed to explore the expression and significance of PD‐L1 in condyloma acuminatum. Methods: The expression of PD‐L1 in the wart of condyloma acuminatum patients and the foreskin of healthy individuals was evaluated. Lentivirus transfection was used to introduce the HPV11‐E7 gene into HaCaT cells to investigate whether HPV infection could affect the expression of PD‐L1. The successfully constructed HPV11‐E7 HaCaT cells were cocultured with Jurkat cells, and Jurkat cell apoptosis and proliferation as well as the Jurkat cell cycle were evaluated by flow cytometry and cell counting kit‐8 (CCK‐8) assays. Results: PD‐L1 was highly expressed in keratinocytes of genital warts. Through the construction of a cell model, we found that HPV11‐E7 could upregulate the expression of PD‐L1 in HaCaT cells. Furthermore, HPV11‐E7 HaCaT cells can promote the apoptosis of Jurkat cells, inhibit the proliferation of Jurkat cells and mediate the cell cycle arrest of Jurkat cells through the PD‐1/PD‐L1 signalling pathway. Conclusions: HPV infection may upregulate PD‐L1 expression in the keratinocytes of genital warts and participate in the inhibition of local T‐cell function. [ABSTRACT FROM AUTHOR]

• The present study firstly applied CNN based on T-SPOT assay to differentiate MTB infection status. • CNN based on T-SPOT assay could successfully discriminate ATB from LTBI. • The diagnostic performance of model based on CNN is better than spot number or TBAg/PHA ratio. The study aims to investigate the potential of convolutional neural network (CNN) based on spot image of T-SPOT assay for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI). CNN was applied to recognize and classify T-SPOT spot image. Logistic regression was used to establish prediction model based on CNN. Areas under the receiver operating characteristic curve (AUCs) of early secreted antigenic target 6 (ESAT-6) CNN, culture filtrate protein 10 (CFP-10) CNN, and phytohemagglutinin (PHA) CNN were more than 0.7 in differentiating ATB from LTBI, while the performance of these indicators was significantly better than that of spot number. Furthermore, prediction model based on the combination of CNNs yielded an AUC of 0.898. The model presented a sensitivity of 85.76% and a specificity of 90.23%. The current study identified CNN based on T-SPOT spot image with the potential to serve as a tool for TB diagnostics. [ABSTRACT FROM AUTHOR]