Objective To investigate the effect of imatinib mesylate on radiation-induced lung injury mice and its influence on the oxidative stress and transforming growth factor-β1 (TGF-β1) expression in mice. Methods Forty-five C57BL/6 mice were divided into a treatment group, a control group and a model group. The treatment group and model group were given radiation of 18 Gy delivered in the thorax. After 4 h daily of the radiation, the treatment group received imatinib mesylate of 0.081 g/kg, while the other groups were given normal saline solution. The experiments were continued for 30 days. After the experiments, the lungs of mice were divided into 4 parts. The haematoxylin and eosin and immunohistochemical stain were prepared to observe the situation of pathology and TGF-β1. The lung homogenate was prepared and the levels of superoxide dismutase (SOD), malondialdehyde (MDA), total antioxidant capacity (T-Aoc) and glutathione peroxidase (GSH-PX) were detected. Results The levels of GSH-PX, T-Aoc and SOD were (173.15±12.21) U, (119.33±11.06) U/mgprot and (1.73±0.33) nmol/mgprot in the treatment group, significantly higher than the control group, while the levels of MDA was (0.68±0.08) nmol/mgprot, significantly lower than the control group (P<0.05). The HE and immunohistochemical stain showed that there were mild alveolar inflammatory changes in the treatment group while such changes were serious in the model group. The scores of HE and immunohistochemical were 1.26±0.12 and 0.31±0.12 in the treatment group, significantly lower than those in the control group (P<0.05). Conclusion The imatinib mesylate can effectively ameliorate the oxidative stress and inhibite TGF-β1 expression in radiation-induced lung injury mice.
ObjectiveTo analyze the risk factors influencing major postoperative complications (MPC) after minimally invasive radical gastrectomy for gastric cancer following neoadjuvant chemotherapy (NACT), and to construct a nomogram for accurately predicting MPC risk factors, and provide a reference for clinical decision-making. MethodsThe gastric cancer patients who underwent minimally invasive radical gastrectomy in the Department of General Surgery of the First Medical Center of the Chinese PLA General Hospital from February 2012 to December 2022 and met the inclusion criteria of this study were retrospectively collected. The univariate and multivariate logistic regression model were used to evaluate the risk factors influencing MPC and a nomogram model was constructed. The MPC were defined as Clavien-Dindo classification grade Ⅱ and beyond. The area under the receiver operating characteristic curve (AUC) and the calibration curve were used to evaluate the discrimination and accuracy of the nomogram model. ResultsA total of 362 patients were included in this study, among whom 65 cases (18.0%) experienced MPC. The multivariate logistic regression analysis showed that the age ≥58 years old, body mass index (BMI) ≥25 kg/m2, tumor long diameter ≥30 mm, operative time ≥300 min, and preoperative neutrophil-to-lymphocyte ratio (NLR) ≥3.7 were the risk factors influencing MPC. The nomogram model constructed using the above variables showed that the AUC (95%CI) was 0.731 (0.662, 0.801) in predicting the risk of MPC. The calibration curves showed that the prediction curve of the nomogram in predicting the MPC was agree well with the actual MPC (Hosmer-Lemeshow test: χ2=9.293, P=0.056). ConclusionFrom the results of this study, nomogram model constructed by combining age, BMI, tumor long diameter, operative time, and preoperative NLR can distinguish between patients with and without MPC after minimally invasive radical gastrectomy for gastric cancer following NACT, and has a better accuracy.