Thrombospondin-1-Derived Peptide RFYVVMWK Improves the Adhesive Phenotype of CD34 <sup>+</sup> Cells from Atherosclerotic Patients with Type 2 Diabetes
Bibliographic record
Abstract
CD34+ progenitor cells are growing in use for vascular repair. However, in diabetic individuals with cardiovascular diseases, these cells have dysfunctional engraftment capabilities, which compromise their use for autologous cell therapy. The thrombospondin-1-derived peptide RFYVVMWK has previously been reported to stimulate cell adhesiveness through CD47 and integrin activation pathways. Our aim was to test whether RFYVVMWK preconditioning could modulate CD34+ cell phenotype and enhance its proadhesive properties in diabetic patients. Peripheral blood mononuclear CD34+ cells isolated from 40 atherosclerotic patients with type 2 diabetes (T2D; n = 20) or without (non-T2D; n = 20) were preconditioned with 30 μM RFYVVMWK or truncated peptide RFYVVM. CD34+ cell adhesion was assessed on a vitronectin-collagen matrix and on TNF-α or IL-1β-stimulated HUVEC monolayers. Adhesion receptors, platelet/CD34+ cell conjugates, and cell viability were analyzed by flow cytometry and confocal microscopy. RFYVVMWK increased the adhesion of T2D CD34+ cells by eightfold to the vitronectin-collagen matrix (p < 0.001) corresponding to a threefold increase compared to unstimulated non-T2D CD34+ cells. The peptide induced the formation of platelet/CD34+ conjugates and increased the expression of TSP-1, CD29, CD51/CD61, and CD62P in both T2D and non-T2D cells. However, RFYVVMWK treatment did not affect the viability/apoptosis of CD34+ progenitor cells. In conclusion, priming CD34+ cells with RFYVVMWK may enhance their vascular engraftment during autologous proangiogenic cell therapy.
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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".