Evidence map›Paper›PMID 31268296›Full record

ArticleACS synthetic biology2019

Displaced by Deceivers: Prevention of Biosensor Cross-Talk Is Pivotal for Successful Biosensor-Based High-Throughput Screening Campaigns.

Lion Konstantin Flachbart, Sascha Sokolowsky, Jan Marienhagen

Open access · hybridAbstract read
In one paragraph

Article in ACS synthetic biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.7field-weighted citation impact, top 9% 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

19 citing papers in PubMed, 53 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Biosensor development for single-cell detection of glucuronate.Journal of industrial microbiology & biotechnology · 2023
    Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Recent trends in biocatalysis.Chemical Society reviews · 2021
    Review
  14. Review
  15. Review
  16. Article
  17. Review
  18. Article
  19. 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

3 authors at 1 institution in 1 country.

Lion Konstantin FlachbartInstitute of Bio- and Geosciences, IBG-1: Biotechnology , Forschungszentrum Jülich , D-52425 Jülich , Germany.
Sascha SokolowskyInstitute of Bio- and Geosciences, IBG-1: Biotechnology , Forschungszentrum Jülich , D-52425 Jülich , Germany.
Jan MarienhagenInstitute of Bio- and Geosciences, IBG-1: Biotechnology , Forschungszentrum Jülich , D-52425 Jülich , Germany.ORCID 0000-0001-5513-3730
Forschungszentrum Jülich · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcriptional biosensors emerged as powerful tools for protein and strain engineering as they link inconspicuous production phenotypes to easily measurable output signals such as fluorescence. When combined with fluorescence-activated cell sorting, transcriptional biosensors enable high throughput screening of vast mutant libraries. Interestingly, even though many published manuscripts describe the construction and characterization of transcriptional biosensors, only very few studies report the successful application of transcriptional biosensors in such high-throughput screening campaigns. Here, we describe construction and characterization of the

Indexed as

Biosensing TechniquesComputational BiologyEscherichia coliFlow CytometryHigh-Throughput Screening AssaysProtein Engineeringdirected evolutionfluorescence-activated cell sortinglibrary screeningproduct sensingprotein engineeringtranscriptional biosensor

Identifiers

PMID31268296
PMCPMC6702586
OpenAlexW2953529417

What OpenQuestion holds

Textmetadata
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.