Glucose oxidase (GOD) is an oxygen-consuming dehydrogenase that can catalyze the production of gluconic acid hydrogen peroxide from glucose, and its specific mechanism of action makes it promising for applications, while the low catalytic activity and poor thermostability have become the main factors limiting the industrial application of this enzyme. In this study, we used the glucose oxidase AtGOD reported with the best thermostability as the source sequence for phylogenetic analysis to obtain the GOD with excellent performance. Six genes were screened and successfully synthesized for functional validation. Among them, the glucose oxidase AhGODB derived from Aspergillus heteromorphus was expressed in Pichia pastoris and showed better thermostability and catalytic activity, with an optimal temperature of 40 ℃, a specific activity of 112.2 U/mg, and a relative activity of 47% after 5 min of treatment at 70 ℃. To improve its activity and thermal stability, we constructed several mutants by directed evolution combined with rational design. Compared with the original enzyme, the mutant T72R/A153P showcased the optimum temperature increasing from 40 to 50 ℃, the specific activity increasing from 112.2 U/mg to 166.1 U/mg, and the relative activity after treatment at 70 ℃ for 30 min increasing from 0% to 33%. In conclusion, the glucose oxidase mutants obtained in this study have improved catalytic activity and thermostability, and have potential for application.
Glucose Oxidase/chemistry* ; Enzyme Stability ; Aspergillus/genetics* ; Pichia/metabolism* ; Temperature ; Catalysis ; Fungal Proteins/metabolism* ; Hot Temperature

Objective To discuss the application of 3D printing technology in the adjuvant treatment of complex Stanford type B aortic dissection(SBAD)and abdominal aortic aneurysm.Methods The clinical data of 64 patients with complex SBAD and 64 patients with abdominal aortic aneurysm,who were admitted to the Mianyang No.404 Hospital of China from January 2022 to January 2024,were retrospectively analyzed.Of the 64 patients with complex SBAD,33 received preoperative 3D printing adjuvant treatment(observation group Ⅰ)and 31 received preoperative routine examination(control group Ⅰ).Of the 64 patients with abdominal aortic aneurysm,32 received preoperative 3D printing adjuvant treatment(observation group Ⅱ)and 32 received preoperative routine examination(control group Ⅱ).The changes in left-right diameter(LR)and anterior-posterior diameter(AP)of anatomical structure in observation group Ⅰ and observation group Ⅱ were analyzed.The perioperative situations were compared between observation group Ⅰ and control group Ⅰ,as well as between observation groupⅡ and control group Ⅱ.Results In patients with complex SBAD,LR of descending aorta diaphragm in S2(STL model)was significantly higher than that in S1(CTA image)and S3(plastic model,P＜0.05),and AP of descending aorta diaphragm in S2 was higher than that in S3(P＜0.05).LR of brachiocephalic trunk in S3 was significantly lower than that in S1 and S2(P＜0.05),AP of brachiocephalic trunk in S3 stage was significantly higher than that in S1 and S2(P＜0.05),and AP of brachiocephalic trunk in S2 was higher than that in S1(P＜0.05).LR of left common carotid artery in S3 was significantly higher than that in S1 and S2(P＜0.05),LR of left common carotid artery in S2 was higher than that in S1(P＜0.05),and AP of left common carotid artery in S3 was lower than that in S1(P＜0.05).LR and AP of left subclavian artery in S3 were significantly higher than those in S1 and S2(P＜0.05).In patients with abdominal aortic aneurysm,LR and AP of tumor neck in S3 were significantly higher than those in S1(P＜0.05),and AP of aneurysm neck in S3 was significantly higher than that in S2(P＜0.05).LR and AP of aneurysm in S3 and S2 were significantly higher than those in S1(P＜0.05),and LR and AP of aneurysm in S3 were significantly higher than those in S2(P＜0.05).LR of abdominal aortic bifurcation in S3 and S2 was significantly higher than that in S1(P＜0.05),LR of abdominal aortic bifurcation in S3 was significantly higher than that in S2(P＜0.05),and AP of abdominal aortic bifurcation in S3 was significantly lower than that in S1(P＜0.05).AP of left common iliac artery in S3 was significantly lower than that in S1(P＜0.05).In the observation group Ⅰ,the operation time,endovascular operation time and length of hospital stay were significantly shorter than those in the control group Ⅰ(P＜0.05),and the intraoperative blood loss and used dosage of contrast agent were lower than those in the control group Ⅰ(P＜0.05).In observation group Ⅱ,the operation time,endovascular operation time and length of hospital stay were significantly shorter than those in the control group Ⅱ(P＜0.05),and the intraoperative blood loss and used dosage of contrast agent were lower than those in the control group Ⅱ(P＜0.05).In patients with complex SBAD or abdominal aortic aneurysm,there was no internal leakage or stent displacement at 6 months after surgery.Conclusion Adjuvant treatment with 3D printing technology is helpful for improving anatomical structure measurement of lesion sites in patients with complex SBAD and abdominal aortic aneurysm.Preoperative 3D plastic model preview surgery is helpful for shortening the operation time and length of hospital stay and reducing the used dosage of contrast agent without affecting surgical treatment effect.

Objective:To establish an acute kidney injury (AKI) prediction model in patients after cardiac surgery by extreme gradient boosting (XGBoost) machine learning model, and to explore the risk and protective factors for AKI in patients after cardiac surgery.Methods:All patients who underwent cardiac surgery in Medical Information Mart for Intensive Care-Ⅲ (MIMIC-Ⅲ) database were enrolled, and they were divided into AKI group and non-AKI group according to whether AKI developed within 14 days after cardiac surgery. Their clinical characteristics were compared. Based on five-fold cross-validation, XGBoost and Logistic regression were used to establish the prediction model of AKI after cardiac surgery. And the area under the receiver operator characteristic curve (AUC) of the models was compared. The output model of XGBoost was interpreted by Shapley additive explanations (SHAP).Results:A total of 6 912 patients were included, of which 5 681 (82.2%) developed AKI within 14 days after the operation, and 1 231 (17.8%) did not. Compared with the non-AKI group, the main characteristics of AKI group included older age [years: 68.0 (59.0, 76.0) vs. 62.0 (52.0, 71.0)], higher incidence of emergency admission and complicated with obesity and diabetes (52.4% vs. 47.8%, 9.0% vs. 4.0%, 32.0% vs. 22.2%), lower respiratory rate [RR; bpm: times/min: 17.0 (14.0, 20.0) vs. 19.0 (15.0, 22.0)], lower heart rate [HR; bpm: 80.0 (67.0, 89.0) vs. 82.0 (71.5, 93.0)], higher blood pressure [mmHg (1 mmHg ≈ 0.133 kPa): 80.0 (70.7, 90.0) vs. 78.0 (70.0, 88.0)], higher hemoglobin (Hb), blood glucose, blood K + level and serum creatinine [SCr; Hb (g/L): 122.0 (109.0, 136.0) vs. 120.0 (106.0, 135.0), blood glucose (mmol/L): 7.3 (6.1, 8.9) vs. 6.8 (5.7, 8.5), blood K + level (mmol/L): 4.2 (3.9, 4.7) vs. 4.2 (3.8, 4.6), SCr (μmol/L): 88.4 (70.7, 106.1) vs. 79.6 (70.7, 97.2)], lower albumin (ALB) and triacylglycerol [TG; ALB (g/L): 38.0 (35.0, 41.0) vs. 39.0 (37.0, 42.0), TG (mmol/L): 1.4 (1.0, 2.0) vs. 1.5 (1.0, 2.2)] as well as higher incidence of multiple organ dysfunction syndrome (MODS) and sepsis (30.6% vs. 16.2%, 3.3% vs. 1.9%), with significant differences (all P < 0.05). In the output model of Logistic regression, important predictors were lactic acid [Lac; odds ratio ( OR) = 1.062, 95% confidence interval (95% CI) was 1.030-1.100, P = 0.005], obesity ( OR = 2.234, 95% CI was 1.900-2.640, P < 0.001), male ( OR = 0.858, 95% CI was 0.794-0.928, P = 0.049), diabetes ( OR = 1.820, 95% CI was 1.680-1.980, P < 0.001) and emergency admission ( OR = 1.278, 95% CI was 1.190-1.380, P < 0.001). Receiver operator characteristic curve (ROC curve) analysis showed that the AUC of the Logistic regression model for predicting AKI after cardiac surgery was 0.62 (95% CI was 0.61-0.67). After optimizing the XGBoost model parameters by grid search combined with five-fold cross-validation, the model was trained well with no overfitting or overfitting. ROC analysis showed that the AUC of XGBoost model for predicting AKI after cardiac surgery was 0.77 (95% CI was 0.75-0.80), which was significantly higher than that of Logistic regression model ( P < 0.01). After SHAP treatment, in the output model of XGBoost, age and ALB were the most important predictors of the final outcome, where age was the risk factor (average |SHAP value| was 0.434), and ALB was the protective factor (average |SHAP value| was 0.221). Conclusions:Age is an important risk factor for AKI after cardiac surgery, and ALB is a protective factor. The performance of machine learning in predicting cardiac and vascular surgery-associated AKI is better than the traditional Logistic regression. XGBoost can analyze the more complex relationship between variables and outcomes, and can predict the risk of postoperative AKI more accurately and individually.