Evidence map›Paper›PMID 27514376›Full record

ReviewCellular and molecular life sciences : CMLS2016

The estrogen-related receptors (ERRs): potential targets against bone loss.

Ling Zhang, Jiemin Wong, Jean-Marc Vanacker

Open access · greenAbstract readReview
In one paragraph

Review in Cellular and molecular life sciences : CMLS, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Review
  3. Regulation of the expression of the estrogen related receptors (ERRs).Cellular and molecular life sciences : CMLS · 2020
    Review
  4. Preceding and subsequent high- and low-trauma fracture patterns-a 13-year epidemiological study in females and males in Austria.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2017
    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

3 authors at 3 institutions in 2 countries.

Ling ZhangInstitut de Génomique Fonctionnelle de Lyon, CNRS UMR5242, Université de Lyon, Université Lyon I, Ecole Normale Supérieure de Lyon, Lyon, France.
Jiemin WongShanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, 200241, China.
Jean-Marc VanackerInstitut de Génomique Fonctionnelle de Lyon, CNRS UMR5242, Université de Lyon, Université Lyon I, Ecole Normale Supérieure de Lyon, Lyon, France. jean-marc.vanacker@ens-lyon.fr.
East China Normal University · CNÉcole Normale Supérieure de Lyon · FRUniversité Claude Bernard Lyon 1 · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bone loss and the resulting skeletal fragility is induced by several pathological or natural conditions, the most prominent of which being aging as well as the decreased levels of circulating estrogens in post-menopause females. To date, most treatments against bone loss aim at preventing excess bone resorption. We here summarize data indicating that the estrogen-related receptors (ERRs) α and γ prevent bone formation. Inhibiting these receptors may thus constitute an anabolic approach by increasing bone formation.

Indexed as

Molecular Targeted TherapyAnimalsBone ResorptionCalcification, PhysiologicHumansLigandsModels, BiologicalReceptors, EstrogenLigandsReceptors, EstrogenBoneERRMenopauseNuclear receptorsOsteoblasts

Identifiers

PMID27514376
PMCPMC11108346
OpenAlexW2514236736

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.