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Vaccines play essential roles in the fight against the COVID-19 pandemic. The development and assessment of COVID-19 vaccines have generally focused on the induction and boosting of neutralizing antibodies targeting the SARS-CoV-2 spike (S) protein. Due to rapid and continuous variation in the S protein, such vaccines need to be regularly updated to match newly emerged dominant variants. T-cell vaccines that target MHC I- or II-restricted epitopes in both structural and non-structural viral proteins have the potential to induce broadly cross-protective and long-lasting responses. In this work, the entire proteome encoded by SARS-CoV-2 (Wuhan-hu-1) is subjected to immunoinformatics-based prediction of HLA-A*02:01-restricted epitopes. The immunogenicity of the predicted epitopes is evaluated using peripheral blood mononuclear cells from convalescent Wuhan-hu-1-infected patients. Furthermore, predicted epitopes that are conserved across major SARS-CoV-2 lineages and variants are used to construct DNA vaccines expressing multi-epitope polypeptides. Most importantly, two DNA vaccine constructs induce epitope-specific CD8 + T-cell responses in a mouse model of HLA-A*02:01 restriction and protect immunized mice from challenge with Wuhan-hu-1 virus after hACE2 transduction. These data provide candidate T-cell epitopes useful for the development of T-cell vaccines against SARS-CoV-2 and demonstrate a strategy for quick T-cell vaccine candidate development applicable to other emerging pathogens.

The real-time video monitoring and alarming of unsafe actions of coal mine personnel is an important means to improve the level of safety in production. The coal mine underground environment is complex, and the monitoring video quality is poor. The conventional action recognition method based on image features or human body key point features is limited in application in the underground coal mine. An action recognition model of multi-feature fusion based on cross-attention mechanism is proposed to recognize unsafe actions of coal mine personnel. For segment video images, the 3D ResNet101 model is adopted to extract image features. The openpose algorithm and ST-GCN (space-time graph convolutional network) are adopted to extract human body key point features. The cross-attention mechanism is used to fuse the image features and human key point features. The fused features are spliced respectively with the image features or human key point features processed by the self-attention mechanism to obtain the final action recognition features. The recognition features is processed by the fully connected layer and the normalized exponential function softmax to obtain action recognition result. Based on the public data sets HMDB51 and UCF101, and the self-built coal mine video dataset, the action recognition experiment is carried out. The results show that the cross-attention mechanism can make the action recognition model more effective in fusing image features and human key point features, and greatly improve the recognition accuracy. At present, SlowFast is the most widely used action recognition model. Compared with the SlowFast, the recognition accuracy of the action recognition model of multi-feature fusion based on cross-attention mechanism has been improved by 1.8% and 0.9% for HMDB51 and UCF101 datasets respectively. The recognition accuracy on the self-built dataset has increased by 6.7%. It is verified that the action recognition model of multi-feature fusion based on cross-attention mechanism is more suitable for the recognition of unsafe actions in the complex coal mine environment.

Background: Immune checkpoint inhibitors (ICIs) have drastically shifted the current landscape toward a wide variety of malignancies. However, ICIs are interrupted owing immune‐related adverse events (irAEs), therapy completion, and disease progression. The risk–benefit of rechallenged ICIs remains inconclusive. Herein, a systematic review and meta‐analysis were conducted to evaluate the safety and efficacy of ICI rechallenge in the treatment of advanced solid tumor. Methods: PubMed, Web of Science, Embase, and Cochrane Library were searched to analyze the efficacy and safety of ICI rechallenge. The study protocol was approved by the PROSPERO International Register of Systematic Reviews (CRD42022372222). The last updated search date was March 2, 2024. Objective response rate (ORR), disease control rate (DCR), overall survival (OS), and incidence rates of all‐ and high‐grade irAEs were evaluated. Results: A total of 41 retrospective studies comprising 2343 patients were ultimately enrolled for qualitative and quantitative assessments. A total of 1200 (51.2%) individuals were male and the median age was 66 years (range 18–97 years). The majority of the tumors was lung cancer (n = 898, 38.3%). The occurrence rates of all‐grade and high‐grade (grade 3 or 4) irAEs between initial and readministration ICIs were not significantly different (all‐grade: OR, 0.75, 95% CI: 0.39–1.45, p = 0.40; I2 = 87%; high‐grade: OR, 0.96, 95% CI: 0.62–1.49, p = 0.87, I2 = 65%). ICIs restart presented a decreased ORR and DCR compared to initial ICI administration (ORR: OR, 0.36, 95% CI: 0.23–0.56, p < 0.00001; I2 = 67%; DCR: OR, 0.62, 95% CI: 0.43–0.89, p = 0.010; I2 = 53%). Seven studies with 513 patients for survival analysis revealed a nonsignificant difference in OS between the ICIs rechallenge and discontinuation cohorts (hazard ratio [HR]: 0.68, 95% confidence interval (CI): 0.35 to 1.35, p = 0.27). Conclusion: Rechallenging immunotherapy is feasible, and patients should be carefully evaluated by a multidisciplinary team prior to initial therapy for close monitoring and assessment of the risk–benefit ratio. Therefore, prospective trials are essential to guide clinicians in the decision‐making process. PROSPERORegistration: CRD42022372222. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to illustrate how a script‐based automated tool can efficiently verify documentation for LDR prostate brachytherapy. Methods and Materials: An in‐house Python‐scripts‐based tool was developed to automatically verify the specific checklists, aligned with our institutional practice guidelines for prostate seed implants (PSI). The scripts, compatible with our radiation oncology information system, could be executed with an optional web‐based middleware to access and evaluate Aria documents. Optimized based on data from the previous 400 patients, the automated tool was applied to a random cohort of 50 LDR patients. It evaluated the adequacy of specific EMR documents by performing checks for data completeness, consistency, and allowable value range. We analyzed the efficiency of using this tool against conventional manual checks in two LDR processes: seed ordering and monthly audits for our PSI programs. Results: The automated tool effectively performed chart checks on the involved PSI documents. Human errors, such as typos and inconsistent information, were identified in 7 out of 50 patients during the seed ordering process and in 2 out of 50 patients during the monthly audit. Meanwhile, this automation reduced the majority of manual chart‐checking time by an average of 5 and 10 min per patient for these processes, respectively. The anticipated efficiency gains will continue to accrue as more check items are digitalized and assessable to the scripts. Conclusions: The implementation of an automated tool tailored for LDR prostate brachytherapy has demonstrated its efficiency benefits. Such an approach can help other clinics substantially enhance routine chart checks, periodic audits, and other applications in similar clinical settings. [ABSTRACT FROM AUTHOR]