Effects of Corticosterone, cAMP, cGMP, Ca^(2+), and Protein Kinase C on Apoptosis of Mouse Thymocytes Induced by X-ray Irradiation

Objective To observe the effects of signal factors of corticosterone （CS）, cAMP, cGMP, Ca^2＋ and protein kinase C （PKC） on lymphocyte apoptosis in mouse thymus induced by X-rays of 4 Gy in vitro. Methods The DNA lyric rate for thymocytes was measured by fluomspectrophotometry. Results The DNA lyric rate for thymocytes 4-8 hours after irradiation with 2-8 Gy was significantly higher than that in the control （P〈0.01）. As compared with the control, the DNA lyric rate for thymocytes treated with 0.01 μnol/L CS （P〈0.01）, 50 ng/mL cAMP （P〈0.01）, 0.05-0.4 μg/mL ionomycin （Iono, P〈0.05 or P〈0.01） or 0.05-0.4 ng/mL phorbol myristate acetate （PMA, P〈0.05 or P〈0.01）, respectively, was significantly increased, while the rate for thymocytes treated with 50 ng/mL cGMP was not significantly increased. The DNA lyric rate for thymocytes treated with 0.01 μmol/L CS （P〈0.01）, 50 ng/mL cAMP （P〈0.01）, 0.2 and 0.4 μg/mL Iono （P〈0.05）, and 0.2 and 0.4 ng/mL PMA （P〈0.05） plus 4-Gy irradiation, respectively, was significantly higher than that treated with single 4-Gy irradiation, while the rate for thymocytes treated with 50 ng/mL cGMP plus 4-Gy irradiation was not increased. When both 0.4 I.tg/mL Iono and 0.4 ng/mL PMA acted on the thymocytes, the DNA lyric rate for thymocytes was significantly higher than that in the control （P〈0.01）, the DNA lytic rate for thymocytes treated with both 0.4 μg/mL Iono and 0.4 ng/mL PMA plus 4-Gy irradiation was significantly higher than that treated with single 4-Gy irradiation （P〈0.05）, but was Iono plus 4-Gy irradiation or 0.4 ng/mL PMA plus 4-Gy irradiation. can promote thymocyte apoptosis induced by larger dose X-rays. not significantly higher than that treated with 0.4 μg/mL Conclusion CS, cAMP, Ca^2＋, and PKC signal factors can promote thymocyte apoptosis induced by larger dose X-rays.

Rupture of sigmoid colon caused by compressed air

Compressed air has been generally used since the beginning of the 20 th century for various applications. However, rupture of the colon caused by compressed air is uncommon. We report a case of pneumatic rupture of the sigmoid colon. The patient was admitted to the emergency room complaining of abdominal pain and distention. His colleague triggered a compressed air nozzle against his anus as a practical joke 2 h previously. On arrival, his pulse rate was 126 beats/min, respiratory rate was 42 breaths/min and blood pressure was 86/54 mm Hg. Physical examination revealed peritoneal irritation and the abdomen was markedly distended. Computed tomography of the abdomen showed a large volume of air in the abdominal cavity. Peritoneocentesis was performed to relieve the tension pneumoperitoneum. Emergency laparotomy was done after controlling shock. Laparotomy revealed a 2-cm perforation in the sigmoid colon. The perforation was sutured and temporary ileostomy was performed as well as thorough drainage and irrigation of the abdominopelvic cavity. Reversal of ileostomy was performed successfully after 3 mo. Follow-up was uneventful. We also present a brief literature review.

Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer

Background: An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use. Methods: Four hundred fourteen patients with rectal cancer discharged between January 2013 and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN. Faster R-CNN based diagnoses were compared with radiologist based diagnoses and pathologist based diagnoses for methodological verification, using correlation analyses and consistency check. For clinical verification, the patients were retrospectively followed up by telephone for 36 months, with post-operative recurrence of rectal cancer as a clinical outcome;recurrence-free survivals of the patients were compared among different diagnostic groups, by methods of Kaplan-Meier and Cox hazards regression model. Results: Significant correlations were observed between any 2 factors among the numbers of metastatic LNs separately diagnosed by radiologists, Faster R-CNN and pathologists, as evidenced by rradiologist-Faster R-CNN of 0.912, rPathologist-radiologist of 0.134, and rPathologist-Faster R-CNN of 0.448 respectively. The value of kappa coefficient in N staging between Faster R-CNN and pathologists was 0.573, and this value between radiologists and pathologists was 0.473. The 3 groups of Faster R-CNN, radiologists and pathologists showed no significant differences in the recurrence-free survival time for stage N0 and N1 patients, but significant differences were found for stage N2 patients. Conclusion: Faster R-CNN surpasses radiologists in the evaluation of pelvic metastatic LNs of rectal cancer, but is not on par with pathologists.

Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network

Background:Colorectal cancer is harmful to the patient’s life.The treatment of patients is determined by accurate preoperative staging.Magnetic resonance imaging(MRI)played an important role in the preoperative examination of patients with rectal cancer,and artificial intelligence(AI)in the learning of images made significant achievements in recent years.Introducing AI into MRI recognition,a stable platform for image recognition and judgment can be established in a short period.This study aimed to establish an automatic diagnostic platform for predicting preoperative T staging of rectal cancer through a deep neural network.Methods:A total of 183 rectal cancer patients’data were collected retrospectively as research objects.Faster region-based convolutional neural networks(Faster R-CNN)were used to build the platform.And the platform was evaluated according to the receiver operating characteristic(ROC)curve.Results:An automatic diagnosis platform for T staging of rectal cancer was established through the study of MRI.The areas under the ROC curve(AUC)were 0.99 in the horizontal plane,0.97 in the sagittal plane,and 0.98 in the coronal plane.In the horizontal plane,the AUC of T1 stage was 1,AUC of T2 stage was 1,AUC of T3 stage was 1,AUC of T4 stage was 1.In the coronal plane,AUC of T1 stage was 0.96,AUC of T2 stage was 0.97,AUC of T3 stage was 0.97,AUC of T4 stage was 0.97.In the sagittal plane,AUC of T1 stage was 0.95,AUC of T2 stage was 0.99,AUC of T3 stage was 0.96,and AUC of T4 stage was 1.00.Conclusion:Faster R-CNN AI might be an effective and objective method to build the platform for predicting rectal cancer T-staging.Trial registration:chictr.org.cn:ChiCTR1900023575;http://www.chictr.org.cn/showproj.aspx?proj=39665.

Development of two-dimensional numerical wave tank based on lattice Boltzmann method

A 2-D numerical wave tank(NWT)is developed using the lattice Boltzmann method(LBM)and a multi-relaxation-time(MRT)collision model coupled with an algebraic volume of fluid(VOF)scheme for free surface tracking.An external force based on the momentum source function is used to generate the waves,and a zone of porous media is used to absorb the waves.Numerical simulations of the progressive and standing waves show that the NWT can generate stable wave trains in agreement with the analytical solutions and eliminate the re-reflection waves.The NWT is used to simulate two problems encountered in practice,namely:the wave transformation over a submerged breakwater and the wave runup on a sea dike.The numerical predictions are in good agreement with the measured data.