Objective: To explore the value of contrast-enhanced computed tomography (CT) radiomics combined with machine learning algorithms in the preoperative prediction of perineural invasion (PNI) in oral squamous cell carcinoma (OSCC), aiming to provide evidence for assisting clinical treatment decision-making. Methods​: This study was approved by the Ethics Committee of the Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. A total of 250 OSCC patients confirmed by postoperative pathology were included, comprising 128 PNI-positive and 122 PNI-negative cases. The dataset was randomly divided into training (n=175), validation (n=38), and independent testing (n=37) sets in a ratio of 7:1.5:1.5. Regions of interest were delineated on preoperative images, and radiomic features were extracted. After dimensionality reduction and feature selection using methods like Least Absolute Shrinkage and Selection Operator (LASSO) regression, multiple machine learning models, including support vector machine (SVM), random forest, Light gradient boosting machine (LightGBM), and a Stacking ensemble model, were constructed. Model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, calibration curves, and decision curve analysis (DCA). Model interpretability was analyzed using Shapley additive explanations (SHAP) and grouped permutation feature importance analysis. Results ​: ​ Among the 250 samples analyzed, the LightGBM model based on radiomics demonstrated the best performance on the independent test set, with an AUC of 0.781, outperforming models like SVM (AUC = 0.730) and Random Forest (AUC = 0.691), as well as clinical models (AUCs ranging 0.549-0.711). The LightGBM model showed good calibration (Brier score 0.198), and DCA indicated high clinical net benefit over a wide threshold probability range. Paired DeLong tests revealed no statistically significant differences in AUC between the ensemble (Stacking) model and the corresponding best-performing radiomics-based model. SHAP analysis and grouped permutation feature importance analysis further indicated that the primary discriminative information for the model came from radiomic texture features. Conclusion ​ The LightGBM model based on contrast-enhanced CT radiomics demonstrated good discriminative ability for preoperative prediction of PNI in OSCC. In the independent test set, it achieved the highest AUC. This model holds promise as a non-invasive auxiliary tool for preoperative risk assessment. Given the limited sample size of the independent test set, these results require further validation in larger cohorts and external datasets.

Objective:To compare the efficiency and characteristics of different carrier proteins and signal sequences in delivering antigens into Escherichia coli outer membrane vesicles (OMVs). Methods:The fusion protein F1V, which consisted of the main protective antigen of Yersinia pestis F1 and LcrV, was expressed using the carrier proteins such as cytolysin A (ClyA), outer membrane protein A (OmpA), or β-lactamases (Bla) signal sequence as a carrier protein. The expression, localization, and content of F1V protein in OMVs were compared and analyzed. Results:All three delivery methods successfully incorporated F1V protein into OMVs and localized it on the surface of OMVs. Notably, when OmpA was used as the carrier protein, the F1V fusion protein constituted up to 30% of the total protein in OMVs. The highest yield of OMVs, reaching 4.2 mg/L, was achieved when Bla signal sequence was used as the carrier.Conclusions:There is a significant difference in the efficiency of different carrier proteins in delivering the F1V antigen into OMVs of Escherichia coli. Considering both the yield of OMVs and the proportion of antigen in the total protein of OMVs, the carrier Bla signal sequence demonstrated the highest efficiency in delivering F1V into OMVs, showing a potential for the future development of OMVs-based plague vaccines.

Objective:To compare the efficiency and characteristics of different carrier proteins and signal sequences in delivering antigens into Escherichia coli outer membrane vesicles (OMVs). Methods:The fusion protein F1V, which consisted of the main protective antigen of Yersinia pestis F1 and LcrV, was expressed using the carrier proteins such as cytolysin A (ClyA), outer membrane protein A (OmpA), or β-lactamases (Bla) signal sequence as a carrier protein. The expression, localization, and content of F1V protein in OMVs were compared and analyzed. Results:All three delivery methods successfully incorporated F1V protein into OMVs and localized it on the surface of OMVs. Notably, when OmpA was used as the carrier protein, the F1V fusion protein constituted up to 30% of the total protein in OMVs. The highest yield of OMVs, reaching 4.2 mg/L, was achieved when Bla signal sequence was used as the carrier.Conclusions:There is a significant difference in the efficiency of different carrier proteins in delivering the F1V antigen into OMVs of Escherichia coli. Considering both the yield of OMVs and the proportion of antigen in the total protein of OMVs, the carrier Bla signal sequence demonstrated the highest efficiency in delivering F1V into OMVs, showing a potential for the future development of OMVs-based plague vaccines.