The Therapeutic Potential of EGCG and Pro-EGCG in Mitigating Ovarian Hyperstimulation Syndrome: Unraveling the Modulatory Mechanism through the VEGF Pathway
Bibliographic record
Abstract
Ovarian hyperstimulation syndrome (OHSS) is a severe complication of controlled ovarian hyperstimulation (COH) during in vitro fertilization (IVF) treatment, characterized by increased capillary permeability.Vascular endothelial growth factor (VEGF) is a key mediator in OHSS, with serum VEGF levels correlating with its severity.In this study, we investigated the therapeutic potential of (-)-epigallocatechin-3-gallate (EGCG) and its derivative, Pro-EGCG, in mitigating OHSS.Using both in vitro and in vivo models, including primary human granulosa-lutein cells, the human granulosa-like tumor KGN cell line, and a rat OHSS model induced with pregnant mare serum gonadotropin, we found that EGCG and Pro-EGCG significantly reduced OHSS progression.This was supported by histological analyses, reductions in ovarian weight, and decreased VEGF expression at both transcriptomic and proteomic levels.Mechanistic studies revealed that EGCG and Pro-EGCG inhibit TGF-β-induced VEGF production through suppression of the TGF-β/Smad and PKA-CREB signaling pathways.RNA sequencing further validated the downregulation of VEGF expression following treatment.These findings highlight the potential of EGCG as a novel adjuvant therapy for managing OHSS, providing a mechanistic basis for its clinical application.
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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".