Publications by authors named "Rufeng Jia"

Magnesium alloy is an excellent material for biodegradable cerebrovascular stents. However, the rapid degradation rate of magnesium alloy will make stent unstable. To improve the biocompatibility of magnesium alloy, in this study, biodegradable sodium alginate and carboxymethyl chitosan (SA/CMCS) was used to coat onto hydrothermally treated the surface of magnesium alloy by a dipping coating method.

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Background: Autophagy plays an important role in the progression of carotid atherosclerosis (CAS). This study aimed to identify hub autophagy-related genes (ATGs) associated with CAS.

Methods: GSE43292 and GSE28829 datasets of early and advanced CAS plaques were enrolled from the Gene Expression Omnibus (GEO) database.

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Background: Intracranial aneurysm (IA) is an uncommon but severe subtype of cerebrovascular disease, with high mortality after aneurysm rupture. Current risk assessments are mainly based on clinical and imaging data. This study aimed to develop a molecular assay tool for optimizing the IA risk monitoring system.

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Objective: To evaluate the safety and efficacy of the SeparGate™ balloon-guiding catheter (BGC) for blocking flow and delivering devices in neurointerventional surgery.

Method: This prospective multicenter single-arm trial enrolled patients who received BGC adjuvant therapy to provide temporary blood flow arrest of the supra-aortic arch arteries and their branch vessels in interventional therapy. The primary endpoint was immediate procedural success rate in flow arrest, device delivery, and withdrawal.

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Article Synopsis
  • Immune inflammation is crucial in the development and rupture of intracranial aneurysms (IAs), but limited understanding of the immune environment has hindered advances in diagnosis and treatment strategies.
  • The study analyzed seven IA datasets using the ssGSEA algorithm, revealing significant immune cell infiltration, activation of immune pathways, and the identification of five key immune-related genes linked to IAs.
  • A pathological feature-derived gene signature (PFDGS) was developed for diagnosing IAs and predicting rupture risk, suggesting that patients with high scores face greater health risks, which can inform clinical decisions for better patient care.
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