FGF2 Signaling Plays an Important Role in Maintaining Pluripotent State of Pig Embryonic Germ Cells
Bibliographic record
Abstract
Germ cells are alternative sources for deriving pluripotent stem cells. Because embryonic germ cells (EGCs) possess physiological and developmental features similar to those of embryonic stem cells, pig EGCs are considered a potential tool for generating transgenic animals for agricultural usage. Therefore, in this study, we attempted to establish and characterize pig EGCs from fetal gonads. EGC lines were derived from the genital ridges of porcine fetuses in media containing leukemia inhibitory factor (LIF), fibroblast growth factor 2 (FGF2), and stem cell factor. After establishment, these cells were cultured and stabilized in LIF- or FGF2-containing media. The cell lines were maintained under both conditions over an extended time period and spontaneously differentiated into the three germ layers in vitro. Interestingly, expression of pluripotency markers showed different patterns between cell lines cultured in LIF or FGF2. SSEA4 was only expressed in FGF2-treated pig EGCs (FGF2-pEGCs), not LIF-treated pig EGCs (LIF-pEGCs). Pluripotency genes were upregulated in FGF2-pEGCs, and germline markers were highly expressed, indicating that FGF2 supplements are more efficient in supporting the pluripotency of pEGCs. In conclusion, we verified that FGF2 signaling plays an important role in reprogramming and maintaining pEGCs from fetal gonads.
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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".