Bioengineered regenerative therapy for diabetes mellitus: establishment of canine mesenchymal stem cell-derived insulin producing cells
Bibliographic record
Abstract
Trend of regenerative therapy for diabetes in human and veterinary practice has conceptually been proven according to Edmonton protocol and animal models. Establishing an alternative insulin-producing cell (IPC) resource is a challenge task for further clinical application. In this study, IPC generation from two practical canine mesenchymal stem cells (cMSCs), canine bone marrow-derived MSCs (cBM-MSCs) and canine adipose-derived MSCs (cAD-MSCs), was of interest. The results illustrated that cBM-MSCs and cAD-MSCs contained distinct pancreatic differentiation potential and required the tailor-made induction protocols. Generation of functional cBM-MSC-derived IPCs needed an integration of genetic and microenvironment manipulation using hanging-drop culture of PDX-1-transfected cBM-MSCs under three-step pancreatic induction protocol. However, this protocol was resource- and time-consumed. Another study on cAD-MSC-derived IPC generation found that IPC-like colonies could be obtained by low attachment culture under three-step induction protocol. Further Notch signaling inhibition during pancreatic endoderm/progenitor induction yielded IPC-like colonies with trend of glucose-responsive C-peptide secretion. Thus, this study showed that IPC-like cells could be obtained from cBM-MSCs and cAD-MSCs by different induction techniques, and further signaling manipulation study should be conducted to maximize the protocol efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".