Evidence map›Paper›PMID 37627808›Full record

ArticleBioengineering (Basel, Switzerland)2023

Programmable Digital-Microfluidic Biochips for SARS-CoV-2 Detection.

Yuxin Wang, Yun-Sheng Chan, Matthew Chae, Donglu Shi, Chen-Yi Lee, Jiajie Diao

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. 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. Article
  2. Review
  3. MonitoringMicromachines · 2024
    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

6 authors.

Yuxin WangDepartment of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.ORCID 0000-0002-4535-2136
Yun-Sheng ChanDepartment of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.
Matthew ChaeDepartment of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.ORCID 0009-0001-3217-6233
Donglu ShiThe Materials Science and Engineering Program, Department of Mechanical and Materials Engineering, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH 45221, USA.ORCID 0000-0002-0837-7780
Chen-Yi LeeInstitute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
Jiajie DiaoDepartment of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.ORCID 0000-0003-4288-3203

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biochips, a novel technology in the field of biomolecular analysis, offer a promising alternative to conventional testing equipment. These chips integrate multiple functions within a single system, providing a compact and efficient solution for various testing needs. For biochips, a pattern-control micro-electrode-dot-array (MEDA) is a new, universally viable design that can replace microchannels and other micro-components. In a Micro Electrode Dot Array (MEDA), each electrode can be programmatically controlled or dynamically grouped, allowing a single chip to fulfill the diverse requirements of different tests. This capability not only enhances flexibility, but also contributes to cost reduction by eliminating the need for multiple specialized chips. In this paper, we present a visible biochip testing system for tracking the entire testing process in real time, and describe our application of the system to detect SARS-CoV-2.

Indexed as

digital-microfluidic biochip (DMFB)loop-mediated isothermal amplification (LAMP)micro-electrode-dot-array (MEDA)programmable biochipSARS-CoV-2

Identifiers

PMID37627808
PMCPMC10451662

What OpenQuestion holds

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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.