EGF-like growth factors upregulate pentraxin 3 expression in human granulosa-lutein cells
Bibliographic record
Abstract
The epidermal growth factor (EGF)-like factors, comprising amphiregulin (AREG), betacellulin (BTC), and epiregulin (EREG), play a critical role in regulating the ovulatory process. Pentraxin 3 (PTX3), an essential ovulatory protein, is necessary for maintaining extracellular matrix (ECM) stability during cumulus expansion. The aim of this study was to investigate the impact of EGF-like factors, AREG, BTC, and EREG on the expression and production of PTX3 in human granulosa-lutein (hGL) cells and the molecular mechanisms involved. Our results demonstrated that AREG, BTC, and EREG could regulate follicular function by upregulating the expression and increasing the production of PTX3 in both primary (obtained from 20 consenting patients undergoing IVF treatment) and immortalized hGL cells. The upregulation of PTX3 expression was primarily facilitated by the activation of the extracellular signal-regulated kinase 1 and 2 (ERK1/2) signaling pathway, induced by these EGF-like factors. In addition, we found that the upregulation of PTX3 expression triggered by the EGF-like factors was completely reversed by either pretreatment with the epidermal growth factor receptor (EGFR) inhibitor, AG1478, or knockdown of EGFR, suggesting that EGFR is crucial for activating the ERK1/2 signaling pathway in hGL cells. Overall, our findings indicate that AREG, BTC, and EREG may modulate human cumulus expansion during the periovulatory stage through the upregulation of PTX3.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".