Platelet-derived growth factors (PDGF-A and -B) and their receptors in human fetal and adult ovaries
Bibliographic record
Abstract
There is no information regarding the presence of platelet-derived growth factors (PDGFs) and their receptors in human ovaries. The expression of PDGF-A, -B and their two receptors, PDGFR-alpha and -beta, was investigated in ovarian samples from women/girls and from human fetuses, at the protein and mRNA levels. The samples were prepared for immunohistochemical staining for PDGF-A and -B and their two receptors and in situ hybridization for the detection of the mRNA transcripts of the receptors. Total RNA was extracted from frozen ovarian samples, and the expression of PDGF-A and -B was investigated by reverse transcription-polymerase chain reaction. The proteins for PDGF-A and -B were detected in oocytes, and in granulosa cells (GC) of 50% of the follicles from women/girls. The proteins and mRNA transcripts for the two receptors were detected in oocytes (mRNA for PDGFR-beta only in 25% of the oocytes). PDGFR-alpha mRNA was expressed in GC of a minority of the samples from women/girls, whereas PDGFR-beta protein and mRNA were identified in over 50% of the GC from this source. PDGF-A and -B transcripts were identified in all the extracts. The presence of the receptors in GC suggests that PDGFs might be involved in the activation of primordial follicles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".