Objective: To investigate the expression of mage-a mRNA in lung cancer tissues of mice induced by coal tar pitch(CTP) fume and to discuss the above model for lung cancer immunotherapy with mage-a. Methods: Tumor tissue samples of lung cancer and paired non-tumor tissues were obtained from 8 lung cancer mice. Total RNA was extracted and cDNA was synthesized. Nested polymerase chain reaction amplification using mage-a specific primer was performed to detect the expression of mage-a. The 2 clones of mage-a mRNA positive PCR products were sequenced by DNAs sequencer (PE-377). Results: Of 29 mice in the experimental group, 8 were induced to lung cancer.Among which 5 (5/8) expressed mage-a mRNA. The expression of mage-a gene was not found in adjacent lung tissues. The DNA sequencing confirmed that the target gene fragments in 2 samples of PCR products were mage-a cDNA. Conclusion: The mage-a gene is highly expressed in lung cancer in mice induced by CTP fume, suggesting that CTP-induced lung cancer in mice may be an ideal animal model for lung cancer therapeutic experiment with MAGE-A.

Objective:Early identification of the sepsis patients accompanied by acute kidney injury (AKI) who were at high risk of invasive mechanical ventilation within 48 hours would help to improve the prognosis.Methods:The clinical information was collected from the sepsis patients with AKI who were admitted at Dongyang People’s Hospital from January 2011 to Octber 2023. The enrolled cases were divided into training set and validation set. The independent risk factors related to invasive mechanical ventilation within 48 hours were obtained by univariable analysis and multivariable logistic regression analysis in the training set, and then a nomogram model was established. The model was evaluated for its discrimination power by the area under the receiver operating characteristic curve (AUC), calibration degree by GiViTI calibration graph and clinical benefit by decision curve analysis both in the training set and validation set. Also, two models (based on SOFA score and NEWs score, respectively) in two patient sets and four models based on machine learning methods (SVM, C5.0, XGBoos and integration method) in validation set were established and compared to logistic model for AUCs by Delong test.Results:A total of 773 cases were finally enrolled in this study. The risk factors for invasive mechanical ventilation within 48 hours were lactate, pro-bnp, D-dimer, saturation of peripheral oxygen and lung infection. The AUC of logistic nomogram model was 0.845 in the training set. In the validation set, the AUC was 0.880, also with good calibration degree and clinical benefit. The AUCs of the models in the training and validation sets based on SOFA score were 0.703 and 0.763, respectively, significantly lower than the logistic model ( P less than 0.05). In addition, the AUCs of the models based on the NEWs score were significantly lower both in the training set (AUC of 0.665, P <0.001) and validation set (AUC of 0.718, P=0.002) than the logistic model. Finally, the AUCs of the models were 0.780 for SVM method, 0.835 for C5.0, 0.798 for XGBoost and 0.813 for integration method, comparable to logistic model ( P > 0.05). Conclusions:The logistic prediction model based on lactate, pro-bnp, D-dimer, saturation of peripheral oxygen and lung infection can efficiently predict the risk for invasive mechanical ventilation within 48 hours among the sepsis patients with AKI.

Objective To evaluate the application of three deep learning algorithms in automatic segmentation of clinical target volumes (CTVs) in high-dose-rate brachytherapy after surgery for endometrial carcinoma. Methods A dataset comprising computed tomography scans from 306 post-surgery patients with endometrial carcinoma was divided into three subsets: 246 cases for training, 30 cases for validation, and 30 cases for testing. Three deep convolutional neural network models, 3D U-Net, 3D Res U-Net, and V-Net, were compared for CTV segmentation. Several commonly used quantitative metrics were employed, i.e., Dice similarity coefficient, Hausdorff distance, 95th percentile of Hausdorff distance, and Intersection over Union. Results During the testing phase, CTV segmentation with 3D U-Net, 3D Res U-Net, and V-Net showed a mean Dice similarity coefficient of 0.90 ± 0.07, 0.95 ± 0.06, and 0.95 ± 0.06, a mean Hausdorff distance of 2.51 ± 1.70, 0.96 ± 1.01, and 0.98 ± 0.95 mm, a mean 95th percentile of Hausdorff distance of 1.33 ± 1.02, 0.65 ± 0.91, and 0.40 ± 0.72 mm, and a mean Intersection over Union of 0.85 ± 0.11, 0.91 ± 0.09, and 0.92 ± 0.09, respectively. Segmentation based on V-Net was similarly to that performed by experienced radiation oncologists. The CTV segmentation time was < 3.2 s, which could save the work time of clinicians. Conclusion V-Net is better than other models in CTV segmentation as indicated by quantitative metrics and clinician assessment. Additionally, the method is highly consistent with the ground truth, reducing inter-doctor variability and treatment time.