Spontaneous Recovery in Directed Dynamical Networks

Complex networked systems,which range from biological systems in the natural world to infrastructure systems in the human-made world,can exhibit spontaneous recovery after a failure;for example,a brain may spontaneously return to normal after a seizure,and traffic flow can become smooth again after a jam.Previous studies on the spontaneous recovery of dynamical networks have been limited to undirected networks.However,most real-world networks are directed.To fill this gap,we build a model in which nodes may alternately fail and recover,and we develop a theoretical tool to analyze the recovery properties of directed dynamical networks.We find that the tool can accurately predict the final fraction of active nodes,and the prediction accuracy decreases as the fraction of bidirectional links in the network increases,which emphasizes the importance of directionality in network dynamics.Due to different initial states,directed dynamical networks may show alternative stable states under the same control parameter,exhibiting hysteresis behavior.In addition,for networks with finite sizes,the fraction of active nodes may jump back and forth between high and low states,mimicking repetitive failure-recovery processes.These findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

Correction:Establishment of a prediction model for prehospital return of spontaneous circulation in out-of-hospital patients with cardiac arrest

This is an erratum to an already published paper named“Establishment of a prediction model for prehospital return of spontaneous circulation in out-ofhospital patients with cardiac arrest”.We found errors in the affiliated institution of the authors.We apologize for our unintentional mistake.Please note,these changes do not affect our results.

Assessment of hindlimb motor recovery affer severe thoracic spinal cord injury in rats: classification of CatWalk XT■ gait analysis parameters

Assessment of locomotion recovery in preclinical studies of experimental spinal cord injury remains challenging. We studied the CatWalk XT■gait analysis for evaluating hindlimb functional recovery in a widely used and clinically relevant thoracic contusion/compression spinal cord injury model in rats. Rats were randomly assigned to either a T9 spinal cord injury or sham laminectomy. Locomotion recovery was assessed using the Basso, Beattie, and Bresnahan open field rating scale and the CatWalk XT■gait analysis. To determine the potential bias from weight changes, corrected hindlimb(H) values(divided by the unaffected forelimb(F) values) were calculated. Six weeks after injury, cyst formation, astrogliosis, and the deposition of chondroitin sulfate glycosaminoglycans were assessed by immunohistochemistry staining. Compared with the baseline, a significant spontaneous recovery could be observed in the CatWalk XT■parameters max intensity, mean intensity, max intensity at%, and max contact mean intensity from 4 weeks after injury onwards. Of note, corrected values(H/F) of CatWalk XT■parameters showed a significantly less vulnerability to the weight changes than absolute values, specifically in static parameters. The corrected CatWalk XT■parameters were positively correlated with the Basso, Beattie, and Bresnahan rating scale scores, cyst formation, the immunointensity of astrogliosis and chondroitin sulfate glycosaminoglycan deposition. The CatWalk XT■gait analysis and especially its static parameters, therefore, seem to be highly useful in assessing spontaneous recovery of hindlimb function after severe thoracic spinal cord injury. Because many CatWalk XT■parameters of the hindlimbs seem to be affected by body weight changes, using their corrected values might be a valuable option to improve this dependency.

Astrocytes in the cerebral cortex play a role in the spontaneous motor recovery following experimental striatal hemorrhage

Intracerebral hemorrhage （ICH） is a stroke subtype caused by spontaneous rupture of small vessels and bleeding into the brain paren- chyma, resulting in cell death and sensorimotor deficits. Despite the greater prevalence of the ischemic form of stroke （87%）, ICH has the highest mortality rate of all stroke subtypes. The striatum is the most affected structure in hemorrhagic stroke （35-70%）, followed by cere- bral cortex （15-30%）, brain stem and cerebellum （5-10%）; patients suffering striatal and/or cortical ICH bear persistent sensorimotor disabilities. Although chronic sensorimotor impairment is established, a considerable amount of patients experience some degree of spontaneous recovery during the first six months after stroke （Qureshi et al., 2009）, and the neurobiological basis of this process is not understood.

Establishment of a prediction model for prehospital return of spontaneous circulation in out-of-hospital patients with cardiac arrest

BACKGROUND Out-of-hospital cardiac arrest(OHCA)is a leading cause of death worldwide.AIM To explore factors influencing prehospital return of spontaneous circulation(P-ROSC)in patients with OHCA and develop a nomogram prediction model.METHODS Clinical data of patients with OHCA in Shenzhen,China,from January 2012 to December 2019 were retrospectively analyzed.Least absolute shrinkage and selection operator(LASSO)regression and multivariate logistic regression were applied to select the optimal factors predicting P-ROSC in patients with OHCA.A nomogram prediction model was established based on these influencing factors.Discrimination and calibration were assessed using receiver operating charac-teristic(ROC)and calibration curves.Decision curve analysis(DCA)was used to evaluate the model’s clinical utility.RESULTS Among the included 2685 patients with OHCA,the P-ROSC incidence was 5.8%.LASSO and multivariate logistic regression analyses showed that age,bystander cardiopulmonary resuscitation(CPR),initial rhythm,CPR duration,ventilation mode,and pathogenesis were independent factors influencing P-ROSC in these patients.The area under the ROC was 0.963.The calibration plot demonstrated that the predicted P-ROSC model was concordant with the actual P-ROSC.The good clinical usability of the prediction model was confirmed using DCA.CONCLUSION The nomogram prediction model could effectively predict the probability of P-ROSC in patients with OHCA.

MPTP Induces Systemic Parkinsonism in Middle-Aged Cynomolgus Monkeys:Clinical Evolution and Outcomes

In this study, we developed a systemic PD model in middle-aged cynomolgus monkeys using individualized low-dose MPTP, to explore effective indicators for the early prediction of clinical outcomes. MPTP was not stopped until the animals showed typical PD motor symptoms on days 10 to 13 after MPTP administration when the Kurlan score reached 10; this abrogated the dif- ferences in individual susceptibility to MPTP. The clinical symptoms persisted, peaking on days 3 to 12 after MPTP withdrawal （rapid progress stage）, and then the Kurlan score plateaued. A Kurlan score at the end of the rapid progress stage 〉15 reflected stable or slowly-progressive PD, while a score 〈15 indicated spontaneous recovery. The entire clinical evolution and outcome of the systemic PD model was characterized in this study, thus providing options for therapeutic and translational research.