Vasoactive Intestinal Peptide and Its Receptors in Human Ovarian Cortical Follicles
Bibliographic record
Abstract
BACKGROUND: Ovarian cryopreservation is one option for fertility preservation in patients with cancer. The danger of reseeding malignancies could be eliminated by in vitro maturation of primordial follicles from the frozen-thawed tissue. However, the development of this system is hindered by uncertainties regarding factors that activate primordial follicles. Neuronal growth factors such as vasoactive intestinal peptide (VIP) play important roles in early mammalian folliculogenesis. There are no data on the expression of VIP and its vasoactive intestinal peptide pituitary adenylate cyclase 1 and 2 receptors (VPAC1-R and VPAC2-R) in human preantral follicles. METHODOLOGY/PRINCIPAL FINDINGS: Tissue samples from 14 human fetal ovaries and 40 ovaries from girls/women were prepared to test for the expression of VIP, VPAC1-R, and VPAC2-R on the protein (immunohistochemisty) and mRNA (reverse transcription polymerase chain reaction) levels. Immunohistochemistry staining was mostly weak, especially in fetal samples. The VIP protein was identified in oocytes and granulosa cells (GCs) in the fetal samples from 22 gestational weeks (GW) onwards. In girls/women, VIP follicular staining (oocytes and GCs) was identified in 45% of samples. VPAC1-R protein was identified in follicles in all fetal samples from 22GW onwards and in 63% of the samples from girls/women (GC staining only in 40%). VPAC2-R protein was identified in follicles in 33% of fetal samples and 47% of the samples from girls/women. The mRNA transcripts for VIP, VPAC1-R, and VPAC2-R were identified in ovarian extracts from fetuses and women. CONCLUSIONS: VIP and its two receptors are expressed in human ovarian preantral follicles. However, their weak staining suggests they have limited roles in early follicular growth. To elucidate if VIP activates human primordial follicles, it should be added to the culture medium.
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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) | 0.002 | 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".