Opening the “Black Box” Underlying Barriers to the Use of Canine Induced Pluripotent Stem Cells: A Narrative Review
Bibliographic record
Abstract
Induced pluripotent stem cells (iPSCs) are produced by resetting the epigenetic and transcriptional landscapes of somatic cells to express the endogenous pluripotency network and revert them back to an undifferentiated state. The reduced ethical concerns associated with iPSCs and their capacity for extensive self-renewal and differentiation make them an unparalleled resource for drug discovery, disease modeling, and novel therapies. Canines (c) share many human diseases and environmental exposures, making them a superior translational model for drug screening and investigating human pathologies compared to other mammals. However, well-defined protocols for legitimate ciPSC production are lacking. Problems during canine somatic cell reprogramming (SCR) yield putative ciPSCs with incomplete pluripotency, at very low efficiencies. Despite the value of ciPSCs, the molecular mechanisms underlying their unsuccessful production and how these may be addressed have not been fully elucidated. Factors, including cost, safety, and feasibility, may also limit the widespread clinical adoption of ciPSCs for treating canine disease. The purpose of this narrative review is to identify barriers to canine SCR on molecular and cellular levels, using comparative research to inform potential solutions to their use in both research and clinical contexts. Current research is opening new doors for the application of ciPSCs in regenerative medicine for the mutual benefit of veterinary and human medicine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".