Adipose-Derived Stem Cells in a Resilient <i>In Situ</i> Forming Hydrogel Modulate Macrophage Phenotype
Bibliographic record
Abstract
Injectable hydrogels have the potential to enhance stem cell-based therapies by improving cell localization, retention, and survival after transplantation. The inflammatory response to both the hydrogel and the encapsulated cells is a critical aspect of this strategy, with macrophages being highly involved in the process of hydrogel remodeling, angiogenesis, and tissue regeneration. As a step toward the development of a cell-based strategy for therapeutic angiogenesis, this work compared the intramuscular injection of allogeneic rat adipose-derived stem/stromal cells (rASCs) in an in situ gelling hydrogel with the injection of the hydrogel alone and rASCs in saline in an immunocompetent rat model by immunohistochemical analysis over 4 weeks. rASCs delivered in the hydrogel were retained intramuscularly at significantly higher densities as compared with cells delivered in saline. The encapsulated rASCs modulated the inflammatory response, promoting CD68 + macrophage recruitment, with the majority of infiltrating cells expressing the M1 marker CCR7, as well as a higher fraction of CD163 + M2c macrophages surrounding the hydrogel. Furthermore, rASCs reduced the initial expression of inducible nitric oxide synthase and promoted arginase-1 expression in the infiltrating macrophages over time, consistent with a shift toward a more proregenerative phenotype. Coincident with the enhanced macrophage infiltration, significantly more CD31 + lumens were observed surrounding and within the hydrogels with rASCs at 2 and 4 weeks as compared with the hydrogels alone. Overall, these results are a promising indication that encapsulated rASCs can have immunomodulatory effects and may enhance angiogenic processes after intramuscular injection, promoting a regenerative macrophage response and blood vessel formation.
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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".