Characterization of Canine Embryonic Stem Cell Lines Derived From Different Niche Microenvironments
Bibliographic record
Abstract
Embryo-derived stem cells hold enormous potential for producing cell-based transplantation therapies, allowing high-throughput drug screening and delineating early embryonic development. However, potential clinical applications must first be tested for safety and efficacy in preclinical animal models. Due to physiological and genetic parity to humans, the domestic dog is widely used as a clinically relevant animal model for cardiovascular, neurodegenerative, orthopedic, and oncologic diseases. Therefore, we established numerous putative canine embryonic stem cell (cESC) lines by immunodissection of the inner cell mass (ICM), which we termed OVC.ID.1-23, and by explant outgrowths from whole canine blastocysts, named OVC.EX.1-16. All characterized lines were immunopositive for OCT4, SOX2, NANOG, SSEA-3, and SSEA-4; displayed high telomerase and alkaline phosphatase (ALP) activities; and were maintained in this state up to 37 passages ( approximately 160 days). Colonies from OVC.EX lines showed classic domed hESC-like morphology surrounded by a ring of fibroblast-like cells, whereas all OVC.ID lines exhibited a mixed cell colony of tightly packed cESCs surrounded by a GATA6+/CDX2- hypoblast-derived support layer. Spontaneous serum-only differentiation without feeder layers demonstrated a strong lineage selection associated with the colony niche type, and not the isolation method. Upon differentiation, cESC lines formed embryoid bodies (EB) comprised of cells representative of all germinal layers, and differentiated into cell types of each layer. Canine ESC lines such as these have the potential to identify differences between embryonic stem cell line derivations, and to develop or to test cell-based transplantation therapies in the dog before attempting human clinical trials.
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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 | 0.001 | 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".