Direct lineage tracing reveals Activin-a potential for improved pancreatic homing of bone marrow mesenchymal stem cells and efficient ß-cell regeneration in vivo
Bibliographic record
Abstract
Abstract Background Despite the potential, bone marrow-derived mesenchymal stem cells (BMSCs) show limitations for beta (ß)-cell replacement therapy due to inefficient methods to deliver BMSCs into pancreatic lineage. In this study, we report TGF-ß family member protein, Activin-a potential to stimulate efficient pancreatic migration, enhanced homing and accelerated ß-cell differentiation. Methods Lineage tracing of permanent green fluorescent protein (GFP)- tagged donor murine BMSCs transplanted either alone or in combination with Activin-a in diabetic mice displayed potential ß-cell regeneration and reversed diabetes. Results Pancreatic histology of Activin-a treated recipient mice reflected high GFP+BMSC infiltration into damaged pancreas with normalized fasting blood glucose and elevated serum insulin. Whole pancreas FACS profiling of GFP+ cells displayed significant homing of GFP+BMSC with Activin-a treatment (6%) compared to BMSCs alone transplanted controls (0.5%). Within islets, approximately 5% GFP+ cells attain ß-cell signature (GFP+ Ins+) with Activin-a treatment versus controls. Further, double immunostaining for mesenchymal stem cell markers CD44+/GFP+ in infiltrated GFP+BMSC deciphers substantial endocrine reprogramming and ß-cell differentiation (6.4% Ins+/GFP+) within 15 days. Conclusion Our investigation thus presents a novel pharmacological approach for stimulating direct migration and homing of therapeutic BMSCs that re-validates BMSC potential for autologous stem cell transplantation therapy in diabetes.
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 | 0.000 |
| 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".