Evidence map›Paper›PMID 37630161›Full record

ArticleMicromachines2023

Digital Microfluidic Multiplex RT-qPCR for SARS-CoV-2 Detection and Variants Discrimination.

Kuan-Lun Ho, Jing Ding, Jia-Shao Fan, Wai Ning Tiffany Tsui, Jianfa Bai, Shih-Kang Fan

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2023. 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
0.5field-weighted citation impact, top 36% of its field
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, 4 citations in OpenAlex.

  1. Review
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

6 authors at 1 institution in 1 country.

Kuan-Lun HoDepartment of Mechanical and Nuclear Engineering, Kansas State University, Manhattan, KS 66506, USA.ORCID 0000-0002-1864-2832
Jing DingDepartment of Mechanical and Nuclear Engineering, Kansas State University, Manhattan, KS 66506, USA.
Jia-Shao FanDepartment of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA.
Wai Ning Tiffany TsuiKansas State Veterinary Diagnostic Laboratory, Kansas State University, Manhattan, KS 66506, USA.
Jianfa BaiKansas State Veterinary Diagnostic Laboratory, Kansas State University, Manhattan, KS 66506, USA.
Shih-Kang FanDepartment of Mechanical and Nuclear Engineering, Kansas State University, Manhattan, KS 66506, USA.
Kansas State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Continuous mutations have occurred in the genome of the SARS-CoV-2 virus since the onset of the COVID-19 pandemic. The increased transmissibility of the mutated viruses has not only imposed medical burdens but also prolonged the duration of the pandemic. A point-of-care (POC) platform that provides multitarget detection will help to track and reduce disease transmissions. Here we detected and discriminated three genotypes of SARS-CoV-2, including the wildtype and two variants of concern (VOCs), the Delta variant and Omicron variant, through reverse transcription quantitative polymerase chain reaction (RT-qPCR) on a digital microfluidics (DMF)-based cartridge. Upon evaluating with the RNA samples of Omicron variant, the DMF RT-qPCR presented a sensitivity of 10 copies/μL and an amplification efficiency of 96.1%, capable for clinical diagnosis. When spiking with SARS-CoV-2 RNA (wildtype, Delta variant, or Omicron variant) and 18S rDNA, the clinical analog samples demonstrated accurate detection and discrimination of different SARS-CoV-2 strains in 49 min.

Indexed as

COVID-19Delta variantdigital microfluidicselectrowettingOmicron variantRT-qPCRSARS-CoV-2

Identifiers

PMID37630161
PMCPMC10456927
OpenAlexW4385949409

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.