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Record W4214884386 · doi:10.1111/rda.14103

Activin A attenuates apoptosis of granulosa cells in atretic follicles through ERβ‐induced autophagy

2022· article· en· W4214884386 on OpenAlexaff
Ning Liu, Shiyou Wang, Qichun Yao, Yuanyou Li, Hai Hu, Xiaorong Tang, Haohan Ran, Christopher A. Price, Zhongliang Jiang

Bibliographic record

VenueReproduction in Domestic Animals · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTGF-β signaling in diseases
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFollicular atresiaApoptosisAutophagyFollicular phaseOvaryFollicleAtresiaBiologyGranulosa cellInternal medicineEndocrinologyOvarian follicleCell biologyAndrologyAnatomyMedicineGenetics

Abstract

fetched live from OpenAlex

It is well known that approximately 99% of ovarian follicles in mammals suffer from a degenerative process known as atresia, which is a huge waste of genetic resource in female animals. Studies have shown that activin A (ACT-A) is located in ovarian granulosa cells and has different effects in granulosa cell depending on species. Although granulosa cells play a critical role during follicular atresia, the mechanism of action of ACT-A in bovine ovarian granulosa cells (BGC) is poorly understood. In this study, we first determined the apoptosis of BGCs isolated from growth follicles and atretic follicles respectively. Then, BGC isolated from atretic follicles were used as a model to elucidate the role of ACT-A in cattle ovary. The results showed that apoptosis occurred in both growing follicles and atretic follicles, and the percentage of apoptotic cells in atretic follicles was higher than that in growing follicles. The current results indicated that ACT-A can attenuate apoptosis of BGC by maintaining the function of BGC in atretic follicles. Increased ERβ induced by ACT-A promoted BGC autophagy but had no effect on apoptosis. In summary, this study suggests that ACT-A attenuates BGC apoptosis in atretic follicles by ERβ-mediated autophagy signalling.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.279
Teacher spread0.263 · 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 teacher head, 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

Citations7
Published2022
Admission routes1
Has abstractyes

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