TGF-β1 induces VEGF expression in human granulosa-lutein cells: a potential mechanism for the pathogenesis of ovarian hyperstimulation syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 it