Enhancing the Migration Ability of Mesenchymal Stromal Cells by Targeting the SDF-1/CXCR4 Axis
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) are currently being investigated in numerous clinical trials of tissue repair and various immunological disorders based on their ability to secrete trophic factors and to modulate inflammatory responses. MSCs have been shown to migrate to sites of injury and inflammation in response to soluble mediators including the chemokine stromal cell-derived factor-(SDF-)1, but during in vitro culture expansion MSCs lose surface expression of key homing receptors particularly of the SDF-1 receptor, CXCR4. Here we review studies on enhancement of SDF-1-directed migration of MSCs with the premise that their improved recruitment could translate to therapeutic benefits. We describe our studies on approaches to increase the CXCR4 expression in in vitro-expanded cord blood-derived MSCs, namely, transfection, using the commercial liposomal reagent IBAfect, chemical treatment with the histone deacetylase inhibitor valproic acid, and exposure to recombinant complement component C1q. These methodologies will be presented in the context of other cell targeting and delivery strategies that exploit pathways involved in MSC migration. Taken together, these findings indicate that MSCs can be manipulated in vitro to enhance their in vivo recruitment and efficacy for tissue repair.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".