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Pengkun Yin on Muck Rack

Pengkun Yin

As seen in: Nature, Royal Society of Chemistry
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Articles

A fully integrated sample-to-answer diagnostic platform enables rapid quantification of post-transplant infection at the point-of-care

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By Pengkun Yin, Lu Gao, Hao Zhang, Jun Zeng, Dan Huang, Lifan Yang, Saifu Yin, Feng LI, Turun Song
| Nature Verified
Abstract Opportunistic infections after organ transplantation require frequent monitoring over years. However, current diagnostic tests are inaccessible to patients in resource-limited settings. Here, we present a microfluidic-enhanced nucleic acid test for on-site reporting (MONITOR), a fully integrated, sample-in-answer-out platform for point-of-care (POC) detection of opportunistic infections.
Open in Who Shared Issue with article?

Multiplexing Steganography Based on Laser-Induced Breakdown Spectroscopy Coupled with Machine Learning

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By Pengkun Yin, Enlai Yang, Youyuan Chen, Zhengying Peng
| Royal Society of Chemistry Verified
Multiplexing Steganography Based on Laser-Induced Breakdown Spectroscopy Coupled with Machine Learning This study proposed steganography based on laser-induced breakdown spectroscopy (LIBS) for the first time. LIBS inks containing different elements with varying concentrations were fabricated to write steganographic text. LIBS combined machine learning as an ideal tool to efficiently extract hidden information.
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