Evidence map›Paper›PMID 36140058›Full record

ReviewBiosensors2022

Recent Advances in Digital Biosensing Technology.

Kathrine Curtin, Bethany J Fike, Brandi Binkley, Toktam Godary, Peng Li

Abstract readReview
In one paragraph

Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. 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

5 authors.

Kathrine CurtinDepartment of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506, USA.ORCID 0000-0002-8070-5355
Bethany J FikeC. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV 26506, USA.
Brandi BinkleyC. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV 26506, USA.ORCID 0000-0003-1572-1364
Toktam GodaryC. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV 26506, USA.
Peng LiC. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV 26506, USA.ORCID 0000-0002-8332-7142

Funding

Development of VSSI-probe technology for in situ probing biological systems using mass spectrometryR01GM135432 · NIGMS · WEST VIRGINIA UNIVERSITY · PI LI, PENG · 2019 to 2023
$1.7M
Division of Electrical, Communications & Cyber Systems 2144216Division of Graduate Education 1102689NIGMS NIH HHS R01 GM135432NIGMS NIH HHS R01GM135432
6 · The paper itself

Abstract

Digital biosensing assays demonstrate remarkable advantages over conventional biosensing systems because of their ability to achieve single-molecule detection and absolute quantification. Unlike traditional low-abundance biomarking screening, digital-based biosensing systems reduce sample volumes significantly to the fL-nL level, which vastly reduces overall reagent consumption, improves reaction time and throughput, and enables high sensitivity and single target detection. This review presents the current technology for compartmentalizing reactions and their applications in detecting proteins and nucleic acids. We also analyze existing challenges and future opportunities associated with digital biosensing and research opportunities for developing integrated digital biosensing systems.

Indexed as

Biosensing TechniquesMicrofluidic Analytical TechniquesNucleic AcidsNanotechnologyProteinsNucleic AcidsProteinsbiosensordigital biosensingdigital ELISAdigital PCRmicrofluidics

Identifiers

PMID36140058
PMCPMC9496261

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.