Injectable Cryopreservable Alginate-Gelatin Microbeads for Pro-Angiogenic Cell Therapy
Bibliographic record
Abstract
Loading cells into biomaterial microbeads is a promising approach to overcome the limitations of mesenchymal stromal cell (MSC) therapy for ischemic tissues, such as low cell survival and retention and variable paracrine function. However, developing a clinically transferable and storable product remains a challenge. Here we developed an oil-free air-assisted coaxial system to encapsulate MSC within injectable alginate-gelatin (ALG-Gel) microbeads and studied their size distribution, injectability, mechanical properties, and stability as a function of gelatin content, as well as the viability, vascular endothelial growth factor (VEGF) release, and paracrine function of MSC in vitro before and after cryopreservation (0.3 M trehalose, 10% DMSO). ALG microbeads containing 0, 3, and 5% w/v gel were produced with a volume-weighted mean diameter (D [4.3]) of 400–500 μm and a low coefficient of variation (14–18%). The microbeads were easily injectable through a 21-gauge needle with only a 3% breakage rate. ALG-Gel3% was found to be the most promising formulation. MSC loaded in ALG-Gel3 presented significantly higher survival and a greater wound closure rate compared to ALG. ALG-Gel3% did not present significantly lower mechanical properties compared to ALG microbeads and was stable for at least 14 days in Hank's balanced salt solution (HBSS). Cryopreservation reduced the mechanical compressive strength and strain at failure, cell survival, and release of VEGF ( p < 0.05), but cell viability remained above 80% after 7 days in culture, and MSC-loaded beads led to rapid human umbilical vein endothelial cells (HUVECs) proliferation and migration in a wound-healing model. Thanks to this method, microbeads contain no trace of oil, which increases their safety and potential for clinical transfer. These cryopreservable oil-free microbeads offer great promise to improve the safety and efficacy of pro-angiogenic MSC-based cell therapy. However, the cryopreservation process should be improved, and in vivo studies in preclinical ischemic models are needed to confirm their therapeutic effectiveness.
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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".