4.8 Article

Automated in Vivo Nanosensing of Breath-Borne Protein Biomarkers

期刊

NANO LETTERS
卷 18, 期 8, 页码 4716-4726

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.nanolett.8b01070

关键词

dLABer; biomarker; particulate matter; toxicity; biosensor; real time; disease monitoring; physiological condition

资金

  1. NSFC [21725701]
  2. National Natural Science Foundation of China [91543126, 21611130103, 21477003, 41121004]
  3. Ministry of Science and Technology [2016YFC0207102, 2015CB553401, 2015DFG92040]

向作者/读者索取更多资源

Toxicology and bedside medical condition monitoring is often desired to be both ultrasensitive and noninvasive. However, current biomarker analyses for these purposes are mostly offline and fail to detect low marker quantities. Here, we report a system called dLABer (detection of living animal's exhaled breath biomarker) that integrates living rats, breath sampling, microfluidics, and biosensors for the automated tracking of breath-borne biomarkers. Our data show that dLABer could selectively detect (online) and report differences (of up to 10(3)-fold) in the levels of inflammation agent interleukin-6 (IL-6) exhaled by rats injected with different ambient particulate matter (PM). The dLABer system was further shown to have an up to 10(4) higher signal-to-noise ratio than that of the enzyme-linked immunosorbent assay (ELISA) when analyzing the same breath samples. In addition, both blood-borne IL-6 levels analyzed via ELISA in rats injected with different PM extracts and PM toxicity determined by a dithiothreitol (DTT) assay agreed well with those determined by the dLABer system. Video recordings further verified that rats exposed to PM with higher toxicity (according to a DTT assay and as revealed by dLABer) appeared to be less physically active. All the data presented here suggest that the dLABer system is capable or real-time, noninvasive monitoring of breath-borne biomarkers with ultrasensitivity. The dLABer system is expected to revolutionize pollutant health effect studies and bedside disease diagnosis as well as physiological condition monitoring at the single-protein level.

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