Tailored generation of insulin producing cells from canine mesenchymal stem cells derived from bone marrow and adipose tissue
Bibliographic record
Abstract
The trend of regenerative therapy for diabetes in human and veterinary practices has conceptually been proven according to the Edmonton protocol and animal models. Establishing an alternative insulin-producing cell (IPC) resource for further clinical application is a challenging task. This study investigated IPC generation from two practical canine mesenchymal stem cells (cMSCs), canine bone marrow-derived MSCs (cBM-MSCs) and canine adipose-derived MSCs (cAD-MSCs). The results illustrated that cBM-MSCs and cAD-MSCs contain distinct pancreatic differentiation potential and require the tailor-made induction protocols. The effective generation of cBM-MSC-derived IPCs needs the integration of genetic and microenvironment manipulation using a hanging-drop culture of PDX1-transfected cBM-MSCs under a three-step pancreatic induction protocol. However, this protocol is resource- and time-consuming. Another study on cAD-MSC-derived IPC generation found that IPC colonies could be obtained by a low attachment culture under the three-step induction protocol. Further, Notch signaling inhibition during pancreatic endoderm/progenitor induction yielded IPC colonies through the trend of glucose-responsive C-peptide secretion. Thus, this study showed that IPCs could be obtained from cBM-MSCs and cAD-MSCs through different induction techniques. Also, further signaling manipulation studies should be conducted to maximize the protocol's efficiency.
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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 | 0.001 |
| 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".