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Xin Zhang

Researcher at Southern Medical University

Publications -  21
Citations -  320

Xin Zhang is an academic researcher from Southern Medical University. The author has contributed to research in topics: Neuroprotection & Angiostrongylus cantonensis. The author has an hindex of 5, co-authored 20 publications receiving 106 citations.

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Preactivation of Notch1 in remote ischemic preconditioning reduces cerebral ischemia-reperfusion injury through crosstalk with the NF-κB pathway

TL;DR: The neuroprotective effect of RIPC against cerebral I/R injury was associated with preactivation of the Notch1 and NF-κB pathways in neurons and significantly improved neurological scores and reduced infarct volume and neuronal apoptosis in rats subjected to I/ R injury.
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Mesenchymal stem cells alleviate the early brain injury of subarachnoid hemorrhage partly by suppression of Notch1-dependent neuroinflammation: involvement of Botch.

TL;DR: BMSCs treatment alleviated neurobehavioral impairments and the inflammatory response in EBI after SAH; these effects may be attributed to Botch upregulation in brain tissue, which subsequently inhibited the Notch1 signaling pathway.
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Heat shock protein 22 modulates NRF1/TFAM-dependent mitochondrial biogenesis and DRP1-sparked mitochondrial apoptosis through AMPK-PGC1α signaling pathway to alleviate the early brain injury of subarachnoid hemorrhage in rats.

TL;DR: In this article, the authors showed that heat shock protein 22 (hsp22) under conditions of stress is a friendly mediator of mitochondrial homeostasis, oxidative stress and apoptosis, thus accelerating neurological recovery.
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Efficacy and Safety of Low-Dose Tirofiban for Acute Intracranial Atherosclerotic Stenosis Related Occlusion with Residual Stenosis after Endovascular Treatment

TL;DR: In patients with residual stenosis after EVT due to acute ICAS-O, a low-dose of tirofiban is associated with favorable functional outcome and reduced incidence of reocclusion without increasing any ICH and sICH.
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Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data.

TL;DR: The Shapley additive explanation (SHAP) analysis was applied to improve the interpretability of the best machine learning model and reveal the reasoning behind the predictions made by the model as mentioned in this paper.