Thermo-rheological responsive microcapsules for time-dependent controlled release of human mesenchymal stromal cells
Bibliographic record
Abstract
Human mesenchymal stromal cells (hMSCs) are adult-source cells that have been extensively evaluated for cell-based therapies. hMSCs delivered by intravascular injection have been reported to accumulate at the sites of injury to promote tissue repair and can also be employed as vectors for the delivery of therapeutic genes. However, the full potential of hMSCs remains limited as the cells are lost after injection due to anoikis and the adverse pathologic environment. Encapsulation of cells has been proposed as a means of increasing cell viability. However, controlling the release of therapeutic cells over time to target tissue still remains a challenge today. Here, we report the design and development of thermo-rheological responsive hydrogels that allow for precise, time dependent controlled-release of hMSCs. The encapsulated hMSCs retained good viability from 76% to 87% dependent upon the hydrogel compositions. We demonstrated the design of different blended hydrogel composites with modulated strength (S parameter) and looseness of hydrogel networks (N parameter) to control the release of hMSCs from thermo-responsive hydrogel capsules. We further showed the feasibility for controlled-release of encapsulated hMSCs within 3D matrix scaffolds. We reported for the first time by a systematic analysis that there is a direct correlation between the thermo-rheological properties associated with the degradation of the hydrogel composite and the cell release kinetics. This work therefore provides new insights into the further development of smart carrier systems for stem cell therapy.
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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".