TGF-β1 induces VEGF expression in human granulosa-lutein cells: a potential mechanism for the pathogenesis of ovarian hyperstimulation syndrome
Bibliographic record
Abstract
Ovarian hyperstimulation syndrome (OHSS) is one of the most serious and iatrogenic complications that can occur during in vitro fertilization treatment. Although the pathogenesis of OHSS is not fully understood, vascular endothelial growth factor (VEGF) has been recognized as an important mediator of the development of OHSS. Transforming growth factor-beta-1 (TGF-β1) is known to regulate various ovarian functions. However, whether VEGF can be regulated by TGF-β1 in human granulosa cells has not been determined. In addition, the role of TGF-β1 in the pathogenesis of OHSS remains unknown. In the present study, we demonstrate that TGF-β1 stimulates VEGF expression in and secretion from both immortalized human granulosa-lutein (hGL) cells and primary hGL cells. Our results demonstrate that the SMAD2/3, ERK1/2, and p38 MAPK signaling pathways are involved in TGF-β1-induced VEGF expression and secretion. Using a mouse OHSS model, we show that the expression levels of TGF-β1 and VEGF are increased in the ovaries of OHSS mice. Blocking TGF-β1 signaling inhibits the development of OHSS by attenuating VEGF expression. Moreover, clinical results reveal that the protein levels of TGF-β1 and VEGF are increased in the follicular fluid of patients with OHSS, and that the levels of these two proteins in the follicular fluid are positively correlated. The results of this study help to elucidate the mechanisms by which VEGF expression is regulated in hGL cells, which could lead to the development of alternative therapeutic approaches for treating OHSS.
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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".