Evidence map›Paper›PMID 29083858›Full record

ArticleACS chemical biology2017

High-Affinity Nucleic-Acid-Based Receptors for Steroids.

Kyung-Ae Yang, Hyosun Chun, Yameng Zhang, Stevan Pecic, Nako Nakatsuka, Anne M Andrews, Tilla S Worgall, Milan N Stojanovic

Abstract read
In one paragraph

Article in ACS chemical biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

40 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Reagentless Real-Time ATP Monitoring with New DNA Aptamers.Small (Weinheim an der Bergstrasse, Germany) · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Dissociation Constant (KSmall methods · 2025
    Review
  15. Article
  16. Article
  17. Generalizable Molecular Switch Designs forAccounts of chemical research · 2025
    Article
  18. Article
  19. Article
  20. 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

8 authors.

Hyosun ChunSchool of Computer Science and Engineering, Seoul National University , Seoul 08826, Korea.
Yameng Zhang
Stevan Pecic
Nako NakatsukaCalifornia NanoSystems Institute, University of California, Los Angeles , Los Angeles, California 90095, United States.ORCID 0000-0001-8248-5248
Anne M AndrewsCalifornia NanoSystems Institute, University of California, Los Angeles , Los Angeles, California 90095, United States.ORCID 0000-0002-1961-4833
Tilla S Worgall
Milan N Stojanovic

Funding

Theranostic Nano-objects: Basic Principles and Initial ApplicationsR01GM104960 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI STOJANOVIC, MILAN N, YAN, HAO · 2012 to 2016
$5.4M
NIGMS NIH HHS R01 GM104960
6 · The paper itself

Abstract

Artificial receptors for hydrophobic molecules usually have moderate affinities and limited selectivities. We describe three new classes of high affinity hydrophobic receptors for nonaromatic steroids based on deoxyribonucleotides, obtained through five high stringency selections coupled with tailored counter-selections. The isolation of multiple classes of high affinity steroid receptors demonstrates the surprising breadth of moderately sized hydrophobic binding motifs (<40 nucleotides) available to natural nucleic acids. Studies of interactions with analogs indicate that two classes, four-way junctions and 4XG

Indexed as

Dehydroepiandrosterone SulfateDesoxycorticosteroneMolecular StructureNucleic AcidsReceptors, SteroidSteroidsDehydroepiandrosterone SulfateDesoxycorticosteroneNucleic AcidsReceptors, SteroidSteroids

Identifiers

PMID29083858
PMCPMC6737899

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.