Research on proactive defense and dynamic repair of complex networks considering cascading effects.

Cascading effects can result in the nonlinear propagation of failures in complex networks, ultimately leading to network collapse. Research on the fault propagation principles, defense strategies, and repair strategies can help mitigate the effects of cascading failures. Especially, proactive defense and dynamic repair are flexible and effective methods to ensure network security. Most studies on the cascade of complex networks are based on the unprocessed initial information of the network. However, marginal nodes are a type of node that cloaks the initial information of the network. In this study, we rank the importance of nodes according to the intensity of network energy confusion after the removal of this node, clarify the meaning of marginal nodes and proposed two methods to screen marginal nodes. The results indicated that the proactive removal of marginal nodes can effectively reduce the effect of cascading failures without causing any negative disturbance to the energy flow of the network. In addition, network repair according to the proposed strategy can minimize the cascade effect in the repair process and improve repair efficiency.

A prospective marker for the prediction of postoperative deep venous thrombosis: Neutrophil extracellular traps.

Deep venous thrombosis (DVT) is a common medical complication in patients with lumbar fractures. The current study aimed to investigate the predictive value of neutrophil extracellular traps (NETs) in postoperative DVT formation in patients with lumbar fractures and to develop a nomogram relating clinical admission information for prediction. Patients who underwent open reduction and pedicle screw internal fixation in the treatment of single-segment lumbar fracture in the Department of Spine Surgery, the First Affiliated Hospital of Nanjing Medical University, from December 2020 to June 2022 were enrolled in this study. Baseline data and laboratory results were collected from enrollees, and the primary study endpoint event was the occurrence of DVT in patients after surgery. Multivariable logistic regression analysis was used to identify risk factors associated with higher odds of DVT after surgery. A nomogram was constructed using the results of the multivariable model. The calibration plot and receiver operating characteristics (ROC) curve were used to show the satisfactory predictive capacity of the model. Of these 393 patients who did not have DVT preoperatively, 79 patients developed it postoperatively, and 314 did not, respectively. Multivariate analysis showed that higher body mass index (BMI) (BMI between 24 and 28: RR = 1.661, 95% CI = 0.891-3.094; BMI ≤28: RR = 5.625, 95% CI = 2.590-12.217; reference: BMI <24), neutrophils (RR = 1.157, 95% CI 1.042-1.285), D-dimer (RR = 1.098, 95% CI 1.000-1.206), and citrullinated histone H3 (CitH3) (RR = 1.043, 95% CI 1.026-1.060) were independent risk factors for postoperative DVT. Using the multivariable analysis, we then constructed a nomogram to predict DVT, which was found to have an area under the curve of 0.757 (95% CI = 0.693-0.820). Calibration plots also showed the satisfied discrimination and calibration of the nomogram. In conclusion, patients with lumbar fractures with postoperative DVT had higher levels of NETs in the circulation preoperatively compared to those without postoperative DVT. Furthermore, based on BMI, D-dimer, neutrophils, and CitH3, we developed a predictive model to predict postoperative DVT incidence in these patients.