Vanadium Core–Shell Nanorods Inspect Metabolic Changes of Diabetic Retinopathy

Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and ranks as the fifth leading cause of visual impairment, but an understanding of DR development has been hampered by the lack of an efficient metabolomic tool. Herein, vanadium core–shell nanorods are developed for...

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Veröffentlicht in:Advanced functional materials 2020-08, Vol.30 (35), p.n/a
Hauptverfasser: Vedarethinam, Vadanasundari, Huang, Lin, Zhang, Minchun, Su, Haiyang, Hu, Huiqian, Xia, Hanping, Liu, Yu, Wu, Beirui, Wan, Xu, Shen, Jie, Xu, Lin, Liu, Wei, Ma, Jing, Qian, Kun
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Sprache:eng
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Zusammenfassung:Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and ranks as the fifth leading cause of visual impairment, but an understanding of DR development has been hampered by the lack of an efficient metabolomic tool. Herein, vanadium core–shell nanorods are developed for metabolic fingerprinting to probe molecular variation in DR. First, a series of vanadium core–shells are constructed with different elemental composition and structural parameters, using silica nanorods to support vanadium oxide. The plasma metabolic fingerprints (MFs) are extracted by the optimized vanadium core–shell nanorod‐assisted laser desorption/ionization mass spectrometry, by analyzing 500 nL of native plasma in seconds. As a result, DR patients are differentiated from non DR controls with a sensitivity of 94% and specificity of 90% using a classification model built on the plasma MFs. Furthermore, DR progression is monitored by a panel of plasma metabolic signatures with gradual changes. This work provides an advanced molecular tool for the metabolomic characterization of DR and may guide the clinical decision making in DR for personalized medicine in the future. Vanadium core–shell nanorod‐assisted laser desorption/ionization mass spectrometry is developed to monitor the blood metabolic changes in diabetic retinopathy (DR). Further combined with the statistical classifiers built using machine learning algorithms, rapid in vitro diagnosis and progression evaluation are achieved for DR. The plasma‐guided approach enables its translation into the clinic, facilitating personalized medicine for various diseases in the near future.
ISSN:1616-301X
1616-3028
DOI:10.1002/adfm.202002791