Evidence map›Paper›PMID 33927298›Full record

ArticleScientific reports2021

A rotationally focused flow (RFF) microfluidic biosensor by density difference for early-stage detectable diagnosis.

Noori Kim, Kyungsup Han, Pei-Chen Su, Insup Kim, Yong-Jin Yoon

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. 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

5 authors.

Noori KimDepartment of Electrical and Electronic Engineering, Newcastle University in Singapore, 172A Ang Mo Kio Avenue 8, 05-01 SIT@NYP Building, Singapore, 567739, Singapore.
Kyungsup HanSchool of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore.
Pei-Chen SuSchool of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore.
Insup KimDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Korea.
Yong-Jin YoonSchool of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore. yongjiny@kaist.ac.kr.

Funding

National Research Foundation of Korea No. NRF-2020R1A2C1011859
6 · The paper itself

Abstract

Label-free optical biosensors have received tremendous attention in point-of-care testing, especially in the emerging pandemic, COVID-19, since they advance toward early-detection, rapid, real-time, ease-of-use, and low-cost paradigms. Protein biomarkers testings require less sample modification process compared to nucleic-acid biomarkers'. However, challenges always are in detecting low-concentration for early-stage diagnosis. Here we present a Rotationally Focused Flow (RFF) method to enhance sensitivity(wavelength shift) of label-free optical sensors by increasing the detection probability of protein-based molecules. The RFF is structured by adding a less-dense fluid to focus the target-fluid in a T-shaped microchannel. It is integrated with label-free silicon microring resonators interacting with biotin-streptavidin. The suggested mechanism has demonstrated 0.19 fM concentration detection along with a significant magnitudes sensitivity enhancement compared to single flow methods. Verified by both CFD simulations and fluorescent flow-experiments, this study provides a promising proof-of-concept platform for next-generation lab-on-a-chip bioanalytics such as ultrafast and early-detection of COVID-19.

Indexed as

BiomarkersBiosensing TechniquesCOVID-19Early DiagnosisHumansLab-On-A-Chip DevicesMicrofluidicsSARS-CoV-2Sensitivity and SpecificityBiomarkers

Identifiers

PMID33927298
PMCPMC8085145

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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.