Effects of Diabetes Mellitus on VEGF-Induced Proliferation Response in Bone Marrow Derived Endothelial Progenitor Cells
Bibliographic record
Abstract
BACKGROUND: This study examined effects of diabetes mellitus (DM) on cellular proliferation associated with vascular endothelial growth factor (VEGF) signaling in endothelial progenitor cells (EPCs) and evaluated protein expression involved in cellular proliferation and proapoptotic signaling in chronically ischemic myocardium. METHODS: Insulin-dependent DM was induced in yucatan miniswine with alloxan. Eight weeks after induction, chronic ischemia was induced by ameroid constrictor placement around the circumflex coronary artery. Seven weeks after ameroid constrictor, perfusion of ischemic territory was measured by isotope-labeled microspheres, and ischemic myocardium was harvested. Bone marrow (BM) samples were harvested from iliac bone and mononuclear cells (MNCs) were cryopreserved. EPCs were isolated from cryopreserved MNCs in control (n = 6) and DM swine (n = 6). EPC proliferation was assessed. RESULTS: EPC proliferation was decreased in DM as compared to control (1.02 ± 0.09, 0.40 ± 0.04, p < 0.01). VEGF-induced EPC proliferation was impaired in DM as compared to control (p < 0.01). Expression of ERK protein, an activator of VEGF-induced cell proliferation, was decreased. AKT activation, an inhibitor of apoptosis, was decreased, while Bad, an activator of proapoptotic signaling, was elevated in the ischemic myocardium from DM. Collateral dependent perfusion was impaired in DM. CONCLUSION: Impaired VEGF-induced proliferation response in EPC as well as an increase in negative myocardial protein expression for cell proliferation and proapoptotic signaling via VEGF could be a therapeutic target to enhance the effects of proangiogenesis therapies in DM and other chronic illnesses.
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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 | 0.001 |
| 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".