Development of a Ligand Trap to Inhibit Follicle-Stimulating Hormone
Bibliographic record
Abstract
Follicle-stimulating hormone (FSH) is essential for ovarian folliculogenesis and female fertility. After menopause, pituitary-derived FSH rises and stays elevated, while estrogen levels decline. Although controversial, some studies claim that chronically elevated FSH drives menopausal co-morbidities, including increased adiposity and decreased bone mass. To investigate whether FSH acts in extragonadal tissues, this study aimed to develop a soluble receptor-based trap to bioneutralize FSH in circulation. The ligand trap is a homodimeric Fc-fusion construct containing two copies of the hormone-binding domain of the human FSH receptor (hFSHR-HBD) fused to the IgG Fc domain. The Fc domain confers solubility to the ligand trap in serum and dimerization, whereas the hormone-binding domain is designed to sequester FSH, thereby blocking or attenuating its actions. To generate an expression construct for recombinant protein production, the hFSHR-HBD was PCR amplified and ligated into an Fc expression vector. The recombinant hFSHR-HBD-Fc construct was transformed into bacteria for plasmid amplification and purified before transfection into mammalian cells. Protein expression in cells was confirmed by western blot. However, the protein was not secreted into the cell culture media for reasons we have not yet determined. Future efforts will focus on optimizing protein expression and secretion by modifying different elements of the vector. If successful, this ligand trap could effectively neutralize FSH, serving as a powerful tool to investigate the hormone’s systemic effects.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".