Addressing the impact of high glucose microenvironment on the immunosuppressive characteristics of human mesenchymal stem cells
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) are a therapeutically efficient type of stem cells validated by their ability to treat many inflammatory and chronic conditions. The biological and therapeutic characteristics of MSCs can be modified depending on the type of microenvironment at the site of transplantation. Diabetes mellitus (DM) is a commonly diagnosed metabolic disease characterized by hyperglycemia, which alters over time the cellular and molecular functions of many cells and causes their damage. Hyperglycemia can also impact the success rate of MSCs transplantation; therefore, it is extremely significant to investigate the effect of high glucose on the biological and therapeutic attributes of MSCs, particularly their immunomodulatory abilities. Thus, in this study, we explored the effect of high glucose on the immunosuppressive characteristics of human adipose tissue-derived mesenchymal stem cells (hAD-MSCs). We found that hAD-MSCs cultured in high glucose lost their immunomodulatory abilities and became detectable by immune cells. The decline in the immunosuppressive capabilities of hAD-MSCs was mediated by significant decrease in the levels of IDO, IL-10, and complement factor H and substantial increase in the activity of immunoproteasome. The protein levels of AMP-activated protein kinase (AMPK) and phosphofructokinase-1 (PFK-1), which are integral regulators of glycolysis, revealed a marked decline in high glucose exposed MSCs. The findings of our study indicated the possibility of immunomodulatory shift in MSCs after being cultured in high glucose, which can be translationally employed to explain their poor survival and short-lived therapeutic outcomes in diabetic patients.
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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) | 0.001 | 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".