Objective:To observe the influence of sevoflurane pretreatment in the lung function of the infants during heart operation by cardiopulmonary bypass (CPB), and to explore its lung protection and possible mechanism.Methods:Sixty infants with ventricular septal defect were enrolled at age less than 1 year old and randomly assigned to pretreatment group and control group (n=30).After the induction of general anesthesia and tracheal intubation,the patients in pretreatment group received continuous inhalation of 1.0 MAC sevoflurane until the beginning of CPB.Inhale sevoflurane was absent in control group.The duration of ventilator support of the infants in two groups was recorded.The Pplate,CL,OI,A-aDO2 ,RI,the number of leukocytes and neutrophils segregated in lung of the patients were compared between two groups at the four time points T0 (before aorta clamping),T1 (30 min after aorta declamping),T2,and T3 (2 h and 6 h after CPB).Results:Compared with control group,the duration of ventilator support of the infants in pretreatment group was obviously shortened (P <0.05).In each group,the CL and OI were significantly decreased (P < 0.05 or P < 0.01),while the Pplate, A-aDO2 ,RI,the number of leukocyte and neutrophils segregated in lung were significantly increased (P <0.05 or P <0.01)at T1,T2,T3 time points compared with T0 time point.The CL and OI in pretreatment group were significantly increased (P <0.05 or P <0.01);the Pplate,A-aDO2,RI,the number of leukocytes and neutrophils segregated in lung in pretreatment group were significantly decreased at T1,T2,and T3 (P <0.05 or P <0.01) compared with control group.Conclusion:Sevoflurane pretreatment might play a role in decreasing the leukocyte adhesion and protecting the lung function in the infants during opening heart operation by CPB.

Objective To develop a convolution neural network (CNN) model to classify multi?sequence MR images of the prostate. Methods ResNet18 convolution neural network (CNN) model was developed to classify multi?sequence MR images of the prostate. A deep residual network was used to improve training accuracy and test accuracy. The dataset used in this experiment included 19 146 7?sequence prostate MR images (transverse T1WI, transverse T2WI, coronal T2WI, sagittal T2WI, transverse DWI, transverse ADC, transverse PWI), from which a total of 2 800 7?sequence MR images was selected as a training set. Three hundred and eighty eight 7?sequence MR images were selected as test sets. Accuracy was used to evaluate the effectiveness of ResNet18 CNN model. Results The classification accuracy of the model for transverse DWI, sagittal T2WI, transverse ADC, transverse T1WI, and transverse T2WI was as high as 100.0% (44/44,52/52), and the accuracy for transverse PWI was also as high as 96.7% (116/120). The accuracy for coronal T2WI was 77.5% (31/40). 0.8% (1/120) of transverse PWI was incorrectly assigned to transverse T2WI, and 2.5% (3/120) incorrectly assigned to sagittal T2WI. 15.0% (6/40) of coronal T2WI was incorrectly assigned to transverse T2WI, and 7.5% (3/40) to sagittal T2WI. Conclusion The experimental results show the effectiveness of our deep learning method regarding accuracy in the prostate multi?sequence MR images detection.

Pyroptosis， an atypical new cell death mode other than apoptosis and necrosis， has been discovered in recent years. Pyroptosis depends on the cleavage of gasdermins （GSDMs） by Caspases. The activated GSDMs act on the plasma membrane to form a perforation， which results in cell lysis and triggers inflammation and immune response. Pyroptosis can be induced by four distinct signaling pathways， including canonical and non-canonical inflammasome pathways， apoptosis-associated Caspases-mediated pathway， and granzyme pathway. In these signaling pathways， GSDMs are the executors of pyroptosis. Pyroptosis is associated with the death of tumor cells and the inflammatory damage of normal tissues. Recent studies have demonstrated that moderate pyroptosis can lead to tumor cell death to exert an anti-tumor effect， and meanwhile stimulate the tumor immune microenvironment， while it can promote tumor development. Despite the good performance， drug-based anti-tumor therapies such as tumor immunotherapy， chemotherapy， and targeted therapy have some shortcomings such as drug resistance， recurrence， and damage to normal tissues. The latest research shows that a variety of natural compounds have anti-tumor effects in the auxiliary treatment of tumors by mediating the pyroptosis pathways in a multi-target and multi-pathway manner， which provide new ideas for the study of anti-tumor therapy. We reviewed the molecular mechanism of pyroptosis and the regulatory role of pyroptosis in tumors and tumor immune microenvironment， and summarized the recent research progress in the natural medicinal components regulating pyroptosis in anti-tumor therapy， with a view to providing ideas for the research on the anti-tumor therapy based on pyroptosis.

Objective: To develop a convolution neural network (CNN) model to classify multi-sequence MR images of the prostate. Methods: ResNet18 convolution neural network (CNN) model was developed to classify multi-sequence MR images of the prostate. A deep residual network was used to improve training accuracy and test accuracy. The dataset used in this experiment included 19 146 7-sequence prostate MR images (transverse T₁WI, transverse T₂WI, coronal T₂WI, sagittal T₂WI, transverse DWI, transverse ADC, transverse PWI), from which a total of 2 800 7-sequence MR images was selected as a training set. Three hundred and eighty eight 7-sequence MR images were selected as test sets. Accuracy was used to evaluate the effectiveness of ResNet18 CNN model. Results: The classification accuracy of the model for transverse DWI, sagittal T₂WI, transverse ADC, transverse T₁WI, and transverse T₂WI was as high as 100.0% (44/44,52/52), and the accuracy for transverse PWI was also as high as 96.7% (116/120). The accuracy for coronal T₂WI was 77.5% (31/40). 0.8% (1/120) of transverse PWI was incorrectly assigned to transverse T₂WI, and 2.5% (3/120) incorrectly assigned to sagittal T₂WI. 15.0% (6/40) of coronal T₂WI was incorrectly assigned to transverse T₂WI, and 7.5% (3/40) to sagittal T₂WI. Conclusion The experimental results show the effectiveness of our deep learning method regarding accuracy in the prostate multi-sequence MR images detection.