Quality-Control Culture System Restores Diabetic Endothelial Progenitor Cell Vasculogenesis and Accelerates Wound Closure
Bibliographic record
Abstract
Delayed diabetic wound healing is, in part, the result of inadequate endothelial progenitor cell (EPC) proliferation, mobilization, and trafficking. Recently, we developed a serum-free functional culture system called the quality and quantity culture (QQc) system that enhances the number and vasculogenic potential of EPCs. We hypothesize that QQc restoration of diabetic EPC function will improve wound closure. To test this hypothesis, we measured diabetic c-kit(+)Sca-1(+)lin(-) (KSL) cell activity in vitro as well as the effect of KSL cell-adoptive transfer on the rate of euglycemic wound closure before and after QQc. KSL cells were magnetically sorted from control and streptozotocin-induced type I diabetic C57BL6J bone marrow. Freshly isolated control and diabetic KSL cells were cultured in QQc for 7 days and pre-QQc and post-QQc KSL function testing. The number of KSL cells significantly increased after QQc for both diabetic subjects and controls, and diabetic KSL increased vasculogenic potential above the fresh control KSL level. Similarly, fresh diabetic cells form fewer tubules, but QQc increases diabetic tubule formation to levels greater than that of fresh control cells (P < 0.05). Adoptive transfer of post-QQc diabetic KSL cells significantly enhances wound closure compared with fresh diabetic KSL cells and equaled wound closure of post-QQc control KSL cells. Post-QQc diabetic KSL enhancement of wound closure is mediated, in part, via a vasculogenic mechanism. This study demonstrates that QQc can reverse diabetic EPC dysfunction and achieve control levels of EPC function. Finally, post-QQc diabetic EPC therapy effectively improved euglycemic wound closure and may improve diabetic wound healing.
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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 | 0.001 | 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".