NIR‑II‑Triggered Composite Nanofibers to Simultaneously Achieve Intracranial Hemostasis,Killing Superbug and Residual Cancer Cells in Brain Tumor Resection Surgery

Malignant glioblastoma(GBM)is prone to relapse due to the inevitable tumor cells residue by surgery.During the tumor resection surgery in brain,addressing bleeding and superbug infections is also full of challenges.Currently,no method or material in clinical craniotomy can simultaneously solve these three problems.Herein,Chitosan composite nanofibers embed-ded with CuSe nanoparticles were prepared by green electrospinning method,in which the CuSe nanoparticles have strong absorption in the second near-infrared(NIR-II)window.Immediately after removing the tumor in craniotomy,nanofibers were electrospun and deposited directly onto the resection site with high precision(>90%)to achieve rapid hemostasis(<8 s).Moreover,evidenced by the deeper penetration depth of NIR-II light(1064 nm)both in the scalp and skull than NIR-I light(808 nm),photothermal and photodynamic therapy induced by NIR-II exhibits efficient superbug-killing rate(>99%)and effectively induces cell apoptosis of residual tumor thereby to inhibit tumor recurrence.Only using the same material,a tril-ogy of intracranial hemostasis,killing superbug and residual cancer cells is simultaneously achieved.The short operation time reduces the risk of craniotomy.This electrospinning strategy could combine with craniotomy and minimally invasive surgery,which may provide novel perspectives in clinical operation besides craniotomy.

Multi-agent graphical games with input constraints:an online learning solution

This paper studies an online iterative algorithm for solving discrete-time multi-agent dynamic graphical games with input constraints.In order to obtain the optimal strategy of each agent,it is necessary to solve a set of coupled Hamilton-Jacobi-Bellman(HJB)equations.It is very difficult to solve HJB equations by the traditional method.The relevant game problem will become more complex if the control input of each agent in the dynamic graphical game is constrained.In this paper,an online iterative algorithm is proposed to find the online solution to dynamic graphical game without the need for drift dynamics of agents.Actually,this algorithm is to find the optimal solution of Bellman equations online.This solution employs a distributed policy iteration process,using only the local information available to each agent.It can be proved that under certain conditions,when each agent updates its own strategy simultaneously,the whole multi-agent system will reach Nash equilibrium.In the process of algorithm implementation,for each agent,two layers of neural networks are used to fit the value function and control strategy,respectively.Finally,a simulation example is given to show the effectiveness of our method.