A stromal cell-derived factor-1 releasing matrix enhances the progenitor cell response and blood vessel growth in ischaemic skeletal muscle
Bibliographic record
Abstract
Although many regenerative cell therapies are being developed to replace or regenerate ischaemic muscle, the lack of vasculature and poor persistence of the therapeutic cells represent major limiting factors to successful tissue restoration. In response to ischaemia, stromal cell-derived factor-1 (SDF-1) is up-regulated by the affected tissue to stimulate stem cell-mediated regenerative responses. Therefore, we encapsulated SDF-1 into alginate microspheres and further incorporated these into an injectable collagen-based matrix in order to improve local delivery. Microsphere-matrix impregnation reduced the time for matrix thermogelation, and also increased the viscosity reached. This double-incorporation prolonged the release of SDF-1, which maintained adhesive and migratory bioactivity, attributed to chemotaxis in response to SDF-1. In vivo, treatment of ischaemic hindlimb muscle with microsphere-matrix led to increased mobilisation of bone marrow-derived progenitor cells, and also improved recruitment of angiogenic cells expressing the SDF-1 receptor (CXCR4) from bone marrow and local tissues. Both matrix and SDF-1-releasing matrix were successful at restoring perfusion, but SDF-1 treatment appeared to play an earlier role, as evidenced by arterioles that are phenotypically older and by increased angiogenic cytokine production, stimulating the generation of a qualitative microenvironment for a rapid and therefore more efficient regeneration. These results support the release of implanted SDF-1 as a promising method for enhancing progenitor cell responses and restoring perfusion to ischaemic tissues via neovascularisation.
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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".