4.8 Article

Renal clearable catalytic gold nanoclusters for in vivo disease monitoring

期刊

NATURE NANOTECHNOLOGY
卷 14, 期 9, 页码 883-+

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NATURE PUBLISHING GROUP
DOI: 10.1038/s41565-019-0527-6

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资金

  1. Koch Institute from the National Cancer Institute (Swanson Biotechnology Center) [P30-CA14051]
  2. National Institute of Environmental Health Sciences [P30-ES002109]
  3. Ludwig Fund for Cancer Research
  4. Koch Institute Marble Center for Cancer Nanomedicine
  5. i-sense Engineering and Physical Sciences Research Council (EPSRC) [EP/K031953/1]
  6. Marshall Aid Commemoration Commission
  7. NIH
  8. National Science Foundation
  9. Ludwig Center for Molecular Oncology
  10. Siebel Scholar Foundation
  11. Wellcome Trust [209121_Z_17_Z]
  12. Swiss National Science Foundation [P2ELP2_178238]
  13. European Research Council (ERC) [616417]
  14. Swiss National Science Foundation (SNF) [P2ELP2_178238] Funding Source: Swiss National Science Foundation (SNF)
  15. EPSRC [EP/K031953/1] Funding Source: UKRI
  16. Wellcome Trust [209121/Z/17/Z] Funding Source: Wellcome Trust

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Ultrasmall gold nanoclusters (AuNCs) have emerged as agile probes for in vivo imaging, as they exhibit exceptional tumour accumulation and efficient renal clearance properties. However, their intrinsic catalytic activity, which can enable an increased detection sensitivity, has yet to be explored for in vivo sensing. By exploiting the peroxidase-mimicking activity of AuNCs and the precise nanometre-size filtration of the kidney, we designed multifunctional protease nanosensors that respond to disease microenvironments to produce a direct colorimetric urinary readout of the disease state in less than one hour. We monitored the catalytic activity of AuNCs in the collected urine of a mouse model of colorectal cancer in which tumour-bearing mice showed a 13-fold increase in colorimetric signal compared to healthy mice. The nanosensors were eliminated completely through hepatic and renal excretion within four weeks of injection with no evidence of toxicity. We envision that this modular approach will enable the rapid detection of a diverse range of diseases by exploiting their specific enzymatic signatures.

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