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Record W4410290375 · doi:10.1071/rd24199

The influence of CCN family proteins on ovarian physiology and pathology

2025· review· en· W4410290375 on OpenAlexaff
El Arbi Abulghasem, Christopher A. Price

Bibliographic record

VenueReproduction Fertility and Development · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective Tissue Growth Factor Research
Canadian institutionsOttawa Fertility CentreCegep de Saint Hyacinthe
FundersMinistry of Education, Libya
KeywordsReproductive technologyReproductive immunologyFolliculogenesisOogenesisBiologyPhysiologyTheriogenologyStem cell biologyReproductive biologyReproductive physiologyReproductive medicineBioinformaticsPathologyMedicineCell biologyEndocrinologyGeneticsOocyteCryopreservationEmbryoPregnancyEmbryogenesis

Abstract

fetched live from OpenAlex

The CCN family of proteins is comprised of six matricellular proteins known to regulate multiple cellular processes such as adhesion, proliferation, differentiation, and apoptosis. CCN proteins are known to function through the binding of integrin receptors and through the regulation of growth factors and cytokines in the context of cardiovascular and skeletal development, injury repair, fibrosis, inflammation and cancer. The expression and roles of several CCNs, particularly CCN1 and CCN2, have been investigated in the ovary as they are effectors of the Hippo signaling pathway, and their role in the development of ovarian fibrosis has been described. Here we review the patterns of expression of CCN1-6 in the ovarian follicle, and the role of CCN2 in follicle development and steroidogenesis, and the expression and potential actions of CCN1-6 in ovarian cancers. We highlight the roles CCNs may play in inflammatory processes, and put forth a case for CCN involvement in the process of ovulation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.324
Teacher spread0.287 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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