Characterization of MSCs expressing islet neogenesis associated protein (INGAP): INGAP secretion and cell survival in vitro and in vivo
Bibliographic record
Abstract
Mesenchymal stem/stromal cells (MSCs) are emerging as a new therapy for diabetes. Here we investigate the properties of MSCs engineered to express Islet Neogenesis Associated Protein (INGAP) previously shown to reverse diabetes in animal models and evaluate their potential for anti-diabetic applications in mice. Mouse bone marrow-derived MSCs retrovirally transduced to co-express INGAP, Firefly Luciferase and EGFP (INGAP-MSCs), were characterized in vitro and implanted intraperitoneally (IP) into non-diabetic and diabetic C57BL/6 mice (Streptozotocin model) and tracked by live bioluminescence imaging (BLI). Distribution and survival of IP injected INGAP-MSCs differed between diabetic and non-diabetic mice, with a rapid clearance of cells in the latter, and a stronger retention (up to 4 weeks) in diabetic mice concurring with homing towards the pancreas. Interestingly, INGAP-MSCs inhibited the progression of hyperglycemia starting at day 3 and lasting for the entire 6 weeks of the study. Pursuing greater retention, we investigated the survival of INGAP-MSCs in hydrogel matrices. When mixed with Matrigel™ and injected subcutaneously into non-diabetic mice, INGAP-MSCs remained in the implant up to 16 weeks. In vitro tests in three matrices (Matrigel™, Type I Collagen and VitroGel®-MSC) demonstrated that INGAP-MSCs survive and secrete INGAP, with best results at the density of 1–2 x 10 6 cells/mL. However, all matrices induced spontaneous adipogenic differentiation of INGAP-MSCs in vitro and in vivo , which requires further investigation of its potential impact on MSC therapeutic properties. In summary, based on their ability to stop the rise in hyperglycemia in STZ-treated mice, INGAP-MSCs are a promising therapeutic tool against diabetes but require further research to improve cell delivery and survival.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".