Topical Application of Culture-Expanded CD34+ Umbilical Cord Blood Cells from Frozen Units Accelerates Healing of Diabetic Skin Wounds in Mice
Bibliographic record
Abstract
Chronic and nonhealing wounds are constant health issues facing patients with type 2 diabetes. As the incidence of type 2 diabetes mellitus (T2DM) increases, the incidence of chronic wounds and amputations will rise. T2DM is associated with peripheral arterial occlusive disease, which leads to the development of nonhealing skin ulcers after minor trauma. Patients develop severe pain limiting their mobility and ability to work and take care of themselves, thus putting a significant burden on the family and society. CD34+ cells from umbilical cord blood (UCB) grown in fibroblast growth factor-4 (FGF-4), stem cell factor, and Flt3-ligand produced a population of cells that have the ability to proliferate and develop properties enabling them to enhance tissue regeneration. The goal of this study was to assess in vitro cultured CD34+ cells in a setting where they would eventually be rejected so we could isolate paracrine signaling mediated therapeutic effect from the therapeutic effect due to engraftment and differentiation. To achieve this, we used db/db mice as a model for diabetic skin ulcers. Here, we report that in vitro cultured UCB CD34+ cells from frozen units can accelerate wound healing and resulted in the regeneration of full thickness skin. This study demonstrates a new indication for banked UCB units in the area of tissue regeneration. Stem Cells Translational Medicine 2018;7:591-601.
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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) | 0.002 | 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".