Evidence map›Paper›PMID 36366271›Full record

ArticleSensors (Basel, Switzerland)2022

Compact Camera Fluorescence Detector for Parallel-Light Lens-Based Real-Time PCR System.

Seul-Bit-Na Koo, Yu-Seop Kim, Chan-Young Park, Deuk-Ju Lee

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Seul-Bit-Na KooSchool of Software, Hallym University, Chuncheon-si 24252, Korea.
Yu-Seop KimSchool of Software, Hallym University, Chuncheon-si 24252, Korea.
Chan-Young ParkSchool of Software, Hallym University, Chuncheon-si 24252, Korea.
Deuk-Ju LeeSchool of Software, Hallym University, Chuncheon-si 24252, Korea.ORCID 0000-0003-3381-0627

Funding

Ministry of Education 2020R1I1A1A01056628Ministry of Education NRF-2020R1I1A3071580
6 · The paper itself

Abstract

The polymerase chain reaction is an important technique in biological research. However, it is time consuming and has a number of disadvantages. Therefore, real-time PCR technology that can be used in real-time monitoring has emerged, and many studies are being conducted regarding its use. Real-time PCR requires many optical components and imaging devices such as expensive, high-performance cameras. Therefore, its cost and assembly process are limitations to its use. Currently, due to the development of smart camera devices, small, inexpensive cameras and various lenses are being developed. In this paper, we present a Compact Camera Fluorescence Detector for use in parallel-light lens-based real-time PCR devices. The proposed system has a simple optical structure, the system cost can be reduced, and the size can be miniaturized. This system only incorporates Fresnel lenses without additional optics in order for the same field of view to be achieved for 25 tubes. In the center of the Fresnel lens, one LED and a complementary metal-oxide semiconductor camera were placed in directions that were as similar as possible. In addition, to achieve the accurate analysis of the results, image processing was used to correct them. As a result of an experiment using a reference fluorescent substance and double-distilled water, it was confirmed that stable fluorescence detection was possible.

Indexed as

LensesOptical DevicesImage Processing, Computer-AssistedOptics and PhotonicsReal-Time Polymerase Chain Reactionfluorescence detectionFresnel lensimage processingopen platform camerareal-time PCR

Identifiers

PMID36366271
PMCPMC9654867

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.