Skip to content

Author

Zi-Yan Zhu

We have 2 of 88 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

A Multidimensional Engineering Strategy Reprograms Microglia via Targeted and Sustained‐Release Extracellular Vesicles for Spinal Cord Injury Repair

ABSTRACT Spinal cord injury (SCI) induces neuroinflammation predominantly mediated by microglia, thereby establishing a detrimental milieu that impedes neurological recovery. Extracellular vesicles (EVs) derived from umbilical cord mesenchymal stem cells (UCMSCs) possess considerable therapeutic potential; however, their clinical translation is constrained by insufficient bioactivity, poor targeting specificity, and uncontrolled release kinetics. Here, we present a multidimensional engineering strategy that overcomes these barriers synergistically. Tetramethylpyrazine (TMP)‐pretreated extracellular vesicles (TEVs) are enriched with anti‐inflammatory and pro‐regenerative factors in their cargo, while Angiopep‐2 (Ang2) peptide‐modified TEVs (Ang‐TEVs) confer significantly enhanced microglial targeting. A reactive oxygen species (ROS)‐responsive hyaluronic acid (HA)‐phenylboronic acid (PBA)/polyvinyl alcohol (PVA) hydrogel serves as an intelligent depot for sustained, on‐demand Ang‐TEVs release at the lesion site. This construct, Ang‐TEVs@Gel, demonstrated robust lesion accumulation and selective microglial uptake. It delivered miR‐664a‐3p, which suppressed PIK3CA to attenuate PI3K‐AKT‐mTOR signaling and unleash autophagic flux, reprogramming microglia toward a reparative state that enhanced myelin debris clearance and quelled inflammation. Consequently, axonal regeneration and remyelination were markedly improved, driving significant motor recovery in SCI mice. By integrating preconditioning, active targeting, and stimuli‐responsive biomaterials, this strategy provides an elegant blueprint for engineering EV‐based therapies to repair the injured central nervous system.

Wu Xiong, Min-Hao Liu, Mingming Zheng et al. · 0 citations
2026

A Distance Distribution-Based Modeling and Analysis for Autonomous Aerial Vehicle Networks

Autonomous aerial vehicles (AAVs) networks, combining AAVs with mobile communication technology, can promote the rational utilization of airspace resources and produce enormous economic value. Due to the complex effects of network deployment areas (NDAs), AAV mobility, and channel fading characteristics, the received signal strength at the AAV exhibits randomness and is susceptible to eavesdropping. However, existing research commonly ignores AAVs’ mobility and only considers the communications and movements within regularly-shaped NDAs. To solve these limitations, we propose a distance distribution-based modeling and analysis framework considering both node randomness and mobility under arbitrarily-shaped convex NDAs. More concretely, this paper focuses on a AAV network for a low-altitude data collection scenario, in which the mobile AAVs serve as an aerial base station to collect the information from the ground randomly distributed Internet of Things (IoT) devices. To involve both the randomness of IoT devices and mobility of AAVs, we propose a method combining random waypoint mobility model and kinematic measure method to derive the distributions of two types of distances for arbitrarily-shaped convex NDAs, including the distance between a random IoT device and a mobile AAV (referred to as R2M) and that between two mobile AAVs (referred to as M2M). Based on the obtained R2M and M2M distance distributions, the communication, coverage, and security performance are derived and analyzed for single-AAV, multi-AAV, and eavesdropping scenarios. The accuracy and effectiveness of the proposed framework are evaluated by extensive numerical studies.

Fei Tong, Yujiao Li, Ziyan Zhu et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.