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Record W3080984767 · doi:10.1016/j.stemcr.2020.07.017

Estrogen Receptor β Controls Muscle Growth and Regeneration in Young Female Mice

2020· article· en· W3080984767 on OpenAlexaff
Daiki Seko, Ryo Fujita, Yuriko Kitajima, Kodai Nakamura, Yuuki Imai, Yusuke Ono

Bibliographic record

VenueStem Cell Reports · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcGill University
FundersMeiji Yasuda Life Foundation of Health and WelfareJapan Agency for Medical Research and DevelopmentJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyUehara Memorial FoundationTakeda Science Foundation
KeywordsBiologyRegeneration (biology)EstrogenEstrogen receptorEndocrinologyInternal medicineReceptorAndrologyCell biologyGeneticsBreast cancerCancer

Abstract

fetched live from OpenAlex

Estrogens are female sex hormones that are important for comprehensively maintaining muscle function, and an insufficiency affects muscle strength and regeneration in females. However, it is still unclear whether estrogen signaling is mediated through receptors. To investigate the specific role of estrogen receptor β (ERβ) in skeletal muscle and satellite cells (muscle stem cells), we generated muscle-specific ERβ-knockout (mKO) and satellite cell-specific ERβ-knockout (scKO) mice, respectively. Young female mKO mice displayed a decrease in fast-type dominant muscle mass. Female, but not male, scKO mice exhibited impaired muscle regeneration following acute muscle injury, probably due to reduced proliferation and increased apoptosis of satellite cells. RNA-sequencing analysis revealed that loss of ERβ in satellite cells altered gene expression of extracellular matrix components, including laminin and collagen. The results indicate that the estrogen-ERβ pathway is a sex-specific regulatory mechanism that controls muscle growth and regeneration in female mice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.207
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations85
Published2020
Admission routes1
Has abstractyes

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