Characterization of the roles of amphiregulin and transforming growth factor β1 in microvasculature-like formation in human granulosa-lutein cells
Bibliographic record
Abstract
Vascular endothelial-cadherin (VE-cadherin) is an essential component that regulates angiogenesis during corpus luteum formation. Amphiregulin (AREG) and transforming growth factor β1 (TGF-β1) are two intrafollicular factors that possess opposite functions in directing corpus luteum development and progesterone synthesis in human granulosa-lutein (hGL) cells. However, whether AREG or TGF-β1 regulates the VE-cadherin expression and subsequent angiogenesis in the human corpus luteum remains to be elucidated. Results showed that hGL cells cultured on Matrigel spontaneously formed capillary-like and sprout-like microvascular networks. Results of specific inhibitor treatment and small interfering RNA-mediated knockdown revealed that AREG promoteed microvascular-like formation in hGL cells by upregulating the VE-cadherin expression mediated by the epidermal growth factor receptor (EGFR)-extracellular signal-regulated kinase1/2 (ERK1/2) signaling pathway. However, TGF-β1 suppressed microvascular-like formation in hGL cells by downregulating VE-cadherin expression mediated by the activin receptor-like kinase (ALK)5-Sma- and Mad-related protein (SMAD)2/3/4 signaling pathway. Collectively, this study provides important insights into the underlying molecular mechanisms by which TGF-β1 and AREG differentially regulate corpus luteum formation in human ovaries.
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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) | 0.000 | 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".