Evidence map›Paper›PMID 31330863›Full record

ArticleSensors (Basel, Switzerland)2019

Optical Temperature Control Unit and Convolutional Neural Network for Colorimetric Detection of Loop-Mediated Isothermal Amplification on a Lab-On-A-Disc Platform.

Da Ye Seul Lim, Moo-Jung Seo, Jae Chern Yoo

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

3 authors.

Da Ye Seul LimCollege of Information and Communication Engineering, Sungkyunkwan University, Suwon, Gyeonggi-Do 440-746, Korea.
Moo-Jung SeoCollege of Information and Communication Engineering, Sungkyunkwan University, Suwon, Gyeonggi-Do 440-746, Korea.
Jae Chern YooCollege of Information and Communication Engineering, Sungkyunkwan University, Suwon, Gyeonggi-Do 440-746, Korea. yoojc@skku.edu.

Funding

MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation) IITP-2019-2018-0-01798
6 · The paper itself

Abstract

Lab-on-a-disc (LOD) has emerged as a promising candidate for a point-of-care testing (POCT) device because it can effectively integrate complex fluid manipulation steps using multiple layers of polymeric substrates. However, it is still highly challenging to design and fabricate temperature measurement and heating system in non-contact with the surface of LOD, which is a prerequisite to successful realization of DNA amplification especially with a rotatable disc. This study presents a Lab-on-a-disc (LOD)-based automatic loop-mediated isothermal amplification (LAMP) system, where a thermochromic coating (<~420 µm) was used to distantly measure the chamber's temperature and a micro graphite film was integrated into the chamber to remotely absorb laser beam with super high efficiency. We used a deep learning network to more consistently analyze the product of LAMP than we could with the naked eye. Consequently, both temperature heating and measurement were carried out without a physical contact with the surface of LOD. The experimental results show that the proposed approach, which no previous work has attempted, was highly effective in realizing LAMP in LOD.

Indexed as

Body TemperatureColorimetryLab-On-A-Chip DevicesPoint-of-Care TestingHumansNeural Networks, ComputerTemperatureconvolutional neural networkhydroxy naphthol bluelab-on-a-discloop-mediated isothermal amplificationpoint-of-care testing

Identifiers

PMID31330863
PMCPMC6679502

What OpenQuestion holds

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LicenceCC BY
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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.