The progress of induced pluripotent stem cells derived from pigs: a mini review of recent advances
Bibliographic record
Abstract
) are widely acknowledged as an important large mammalian animal model due to their similarity to human physiology, genetics, and immunology. Leveraging the full potential of this model presents significant opportunities for major advancements in the fields of comparative biology, disease modeling, and regenerative medicine. Thus, the derivation of pluripotent stem cells from this species can offer new tools for disease modeling and serve as a stepping stone to test future autologous or allogeneic cell-based therapies. Over the past few decades, great progress has been made in establishing porcine pluripotent stem cells (pPSCs), including embryonic stem cells (pESCs) derived from pre- and peri-implantation embryos, and porcine induced pluripotent stem cells (piPSCs) using a variety of cellular reprogramming strategies. However, the stabilization of pPSCs was not as straightforward as directly applying the culture conditions developed and optimized for murine or primate PSCs. Therefore, it has historically been challenging to establish stable pPSC lines that could pass stringent pluripotency tests. Here, we review recent advances in the establishment of stable porcine PSCs. We focus on the evolving derivation methods that eventually led to the establishment of pESCs and transgene-free piPSCs, as well as current challenges and opportunities in this rapidly advancing field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".