Bibliographic record
Abstract
Generating oocytes from cells derived from skin in vitro may provide a valuable model for identifying factors involved in germ cell formation and oocyte differentiation. In addition, the "oocytes" produced could potentially be useful for therapeutic cloning, and thus offer new possibilities for tissue therapy. We recently reported the differentiation of cells derived from porcine fetal skin into cells resembling germ cells and oocytes. A subpopulation of these cells expressed germ cell markers and formed aggregate like oocyte-cumulus complexes that secreted ovarian steroid hormones and responded to gonadotropin stimulation. Some of these aggregates extruded large oocyte-like cells that expressed markers appropriate to oocytes. We now show further evidence of germ cell marker expression during differentiation. We have also compared the oocyte-like cells with natural oocytes for their expression levels of Oct4, growth differentiation factor-9b (GDF9b), the deleted in azoospermia -like (DAZL) gene, vasa, zona pellucida (ZP), and the meiosis marker synaptonemal complex protein 3 (SCP3), and have revealed interesting similarities and differences.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".