<scp>Short‐Term</scp> exposure of umbilical cord blood <scp>CD34</scp>+ cells to human platelet lysate and cytokines enhances engraftment
Bibliographic record
Abstract
BACKGROUND: Intra bone marrow (IBM) injection has been proposed as a strategy to bypass homing inefficiencies associated with intravenous (IV) hematopoietic progenitor stem cell (HSPC) transplantation and thus increases the number of HSPC that engraft. Despite physical delivery into the bone marrow cavity, many donor cells are rapidly redistributed by vascular perfusion. Thus, the objective of our study was to evaluate the ability of human platelet lysates (hPL) to improve HSPC retention into the bone marrow and consequently to improve engraftment. STUDY DESIGN AND METHODS: HSPC were isolated from human umbilical cord blood. HSPC were seeded in the wells of a 24-well microplate and exposed to increasing concentrations of hPL with or without cytokines for 24 hours. Following priming, HSPC cells chemotaxis to rhSDF-1 was determined in vitro and engraftment in NSG mice was evaluated. RESULTS: Priming of cord blood CD34+ cells to a combination of hPL and cytokines resulted in a significant increase (up to 3-fold) in the expression of the CD34 antigen on HSPC. This effect was closely correlated to a significantly increased (up to 7-fold) migration toward a rhSDF-1 concentration gradient. In addition, IBM injection of CD34+ cells previously primed with hPL+cytokines into NSG mice showed significantly increased engraftment as measured by human platelet numbers, human CD45 and human CD34+ cells for unprimed and primed cells, respectively. CONCLUSION: The use of hPL + cytokines as a short-term priming treatment for UCB could be an advantageous strategy to improve clinical outcomes following IBM injection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".