Evidence map›Paper›PMID 27868127›Full record

ArticleLab on a chip2016

Digital DNA detection based on a compact optofluidic laser with ultra-low sample consumption.

Wonsuk Lee, Qiushu Chen, Xudong Fan, Dong Ki Yoon

Abstract read
In one paragraph

Article in Lab on a chip, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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  4. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Wonsuk LeeGraduate School of Nanoscience and Technology and KINC, KAIST, Daejeon, 305-701, Republic of Korea. nandk@kaist.ac.kr.
Qiushu ChenDepartment of Biomedical Engineering, University of Michigan, 1101 Beal Ave., Ann Arbor, MI 48109, USA. xsfan@umich.edu.
Xudong FanDepartment of Biomedical Engineering, University of Michigan, 1101 Beal Ave., Ann Arbor, MI 48109, USA. xsfan@umich.edu.
Dong Ki YoonGraduate School of Nanoscience and Technology and KINC, KAIST, Daejeon, 305-701, Republic of Korea. nandk@kaist.ac.kr.

Funding

Protein interaction study In-vitro and in live cells with optofluidic lasersR21EB016783 · NIBIB · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FAN, XUDONG · 2014 to 2015
$403k
NIBIB NIH HHS R21 EB016783
6 · The paper itself

Abstract

DNA lasers self-amplify optical signals from a DNA analyte as well as thermodynamic differences between sequences, allowing quasi-digital DNA detection. However, these systems have drawbacks, such as relatively large sample consumption and complicated dye labelling. Moreover, although the lasing signal can detect the target DNA, it is superimposed on an unintended fluorescence background, which persists for non-target DNA samples as well. From an optical point of view, it is thus not truly digital detection and requires spectral analysis to identify the target. In this work, we propose and demonstrate an optofluidic laser that has a single layer of DNA molecules as the gain material. A target DNA produces intensive laser emission comparable to existing DNA lasers, while any unnecessary fluorescence background is successfully suppressed. As a result, the target DNA can be detected with a single laser pulse, in a truly digital manner. Since the DNA molecules cover only a single layer on the surface of the laser microcavity, the DNA sample consumption is a few orders of magnitude lower than that of existing DNA lasers. Furthermore, the DNA molecules are stained by simply immersing the microcavity in the intercalating dye solution, and thus the proposed DNA laser is free of any complex dye-labelling process prior to analysis.

Indexed as

LasersOptical DevicesDNALimit of DetectionDNA

Identifiers

PMID27868127
PMCPMC5137248

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.