Type 2 diabetes alters mesenchymal stem cell secretome composition and angiogenic properties
Bibliographic record
Abstract
This study aimed at characterizing the impact of type 2 diabetes mellitus (T2DM) on the bone marrow mesenchymal stem cell (BMMSC) secretome and angiogenic properties. BMMSCs from Zucker diabetic fatty rats (ZDF) (a T2DM model) and Zucker LEAN littermates (control) were cultured. The supernatant conditioned media (CM) from BMMSCs of diabetic and control rats were collected and analysed. Compared to results obtained using CM from LEAN-BMMSCs, the bioactive content of ZDF-BMMSC CM (i) differently affects endothelial cell (HUVEC) functions in vitro by inducing increased (3.5-fold; P < 0.01) formation of tubule-like structures and migration of these cells (3-fold; P < 0.001), as well as promotes improved vascular formation in vivo, and (ii) contains different levels of angiogenic factors (e.g. IGF1) and mediators, such as OSTP, CATD, FMOD LTBP1 and LTBP2, which are involved in angiogenesis and/or extracellular matrix composition. Addition of neutralizing antibodies against IGF-1, LTBP1 or LTBP2 in the CM of BMMSCs from diabetic rats decreased its stimulatory effect on HUVEC migration by approximately 60%, 40% or 40%, respectively. These results demonstrate that BMMSCs from T2DM rats have a unique secretome with distinct angiogenic properties and provide new insights into the role of BMMSCs in aberrant angiogenesis in the diabetic milieu.
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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".