Evidence map›Paper›PMID 28165341›Full record

ReviewThe Journal of clinical investigation2017

Nuclear receptors: emerging drug targets for parasitic diseases.

Zhu Wang, Nathaniel E Schaffer, Steven A Kliewer, David J Mangelsdorf

Open access · bronzeAbstract readReview
In one paragraph

Review in The Journal of clinical investigation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.4field-weighted citation impact, top 23% 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

16 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Molecular Cloning and Characterization of aPathogens (Basel, Switzerland) · 2022
    Article
  9. Article
  10. Identification of a nuclear receptor/coactivator developmental signaling pathway in the nematode parasiteProceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  11. Transcription Factors Interplay Orchestrates the Immune-Metabolic Response ofFrontiers in cellular and infection microbiology · 2021
    Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. 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

4 authors at 1 institution in 1 country.

Zhu Wang
Nathaniel E Schaffer
Steven A Kliewer
David J Mangelsdorf
Howard Hughes Medical Institute · US

Funding

Mechanisms and Treatment of Chronic, Latent Human StrongyloidiasisR33AI105856 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI ABRAHAM, DAVID, LOK, JAMES B · 2015 to 2017
$1.4M
NIAID NIH HHS R33 AI105856
6 · The paper itself

Abstract

Parasitic worms infect billions of people worldwide. Current treatments rely on a small group of drugs that have been used for decades. A shortcoming of these drugs is their inability to target the intractable infectious stage of the parasite. As well-known therapeutic targets in mammals, nuclear receptors have begun to be studied in parasitic worms, where they are widely distributed and play key roles in governing metabolic and developmental transcriptional networks. One such nuclear receptor is DAF-12, which is required for normal nematode development, including the all-important infectious stage. Here we review the emerging literature that implicates DAF-12 and potentially other nuclear receptors as novel anthelmintic targets.

Indexed as

Helminth ProteinsNematodaReceptors, Cytoplasmic and NuclearAnimalsAntinematodal AgentsDrug Delivery SystemsHumansNematode InfectionsAntinematodal AgentsHelminth ProteinsReceptors, Cytoplasmic and Nuclear

Identifiers

PMID28165341
PMCPMC5373876
OpenAlexW2585772060

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.