Human Mesenchymal Stem Cells Protect Human Islets from Pro-Inflammatory Cytokines
Bibliographic record
Abstract
Transplantation of human islets is an attractive alternative to daily insulin injections for patients with type 1 diabetes. However, the majority of islet recipients lose graft function within five years. Inflammation is a primary contributor to graft loss, and inhibiting pro-inflammatory cytokine activity can reverse inflammation mediated dysfunction of islet grafts. As mesenchymal stem cells (MSCs) possess numerous immunoregulatory properties, we hypothesized that MSCs could protect human islets from pro-inflammatory cytokines. Five hundred human islets were co-cultured with 0.5 or 1.0 × 10(6) human MSCs derived from bone marrow or pancreas for 24 hours followed by 48 hour exposure to interferon-γ, tumor necrosis factor-α and interleukin 1β. Controls include islets cultured alone (± cytokines) and with human dermal fibroblasts (± cytokines). For all conditions, glucose stimulated insulin secretion (GSIS), total islet cellular insulin content, islet β cell apoptosis, and potential cytoprotective factors secreted in the culture media were determined. Cytokine exposure disrupted human islet GSIS based on stimulation index and percentage insulin secretion. Conversely, culture with 1.0 × 10(6) bMSCs preserved GSIS from cytokine treated islets. Protective effects were not observed with fibroblasts, indicating that preservation of human islet GSIS after exposure to pro-inflammatory cytokines is MSC dependent. Islet β cell apoptosis was observed in the presence of cytokines; however, culture of bMSCs with islets prevented β cell apoptosis after cytokine treatment. Hepatocyte growth factor (HGF) as well as matrix metalloproteinases 2 and 9 were also identified as putative secreted cytoprotective factors; however, other secreted factors likely play a role in protection. This study, therefore, demonstrates that MSCs may be beneficial for islet engraftment by promoting cell survival and reduced inflammation.
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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) | 0.002 | 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".