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Updated: Jan 28, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Engineered ETS1-Nanoconjugate Restores Immune Homeostasis through Dual Immune-Vascular Modulation in Relapsing and
Feng Zhang1, Si-Bo Yang1, Yan Luo1
1Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Abstract:
While current treatments for multiple sclerosis (MS) targeting T or B cells have shown clinical benefit, they remain insufficient due to the multifactorial and dynamic nature of MS pathogenesis. A key obstacle to restoring immune homeostasis lies in the reciprocal reinforcement between endothelial-to-mesenchymal transition (EndMT) and sustained immune infiltration, which collectively exacerbate blood-brain barrier (BBB) disruption and neuroinflammation. We identified IL7R as a shared target on endothelial cells and pathogenic T cells via scRNA-seq and developed a multifunctional nanodelivery platform-ETS1 pDNA/PBAE@ITP-MM (IMNP)-comprising ETS1 plasmid DNA complexed with poly(β-amino ester) (PBAE), an interleukin-7 receptor (IL7R)-targeting peptide (ITP), and a macrophage membrane (MM) coating. IMNP concurrently modulates endothelial cells and T lymphocytes for synergistic efficacy. Exploiting the intrinsic inflammation-targeting capacity of activated macrophage membranes and ITP conjugation, IMNPs preferentially accumulate at the vascular endothelium, where they inhibit EndMT and preserve BBB integrity. Subsequently, IMNPs attenuate differentiation of CD4+ T cells into proinflammatory Th1/Th17 subsets, thereby reducing CNS infiltration and re-establishing immune microenvironmental balance in both relapsing-remitting MS (RRMS) and secondary progressive MS (SPMS) models. Our findings provide proof-of-concept for a biomimetic nanoplatform that achieves dual vascular-immune modulation, significantly alleviating neuroinflammation, reducing demyelination, and improving motor performance in both RRMS and SPMS models.
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