Hydrogel Encapsulation of Mesenchymal Stem Cells and Their Derived Exosomes for Tissue Engineering
Bibliographic record
Abstract
Tissue engineering has been an inveterate area in the field of regenerative medicine for several decades. However, there remains limitations to engineer and regenerate tissues. Targeted therapies using cell-encapsulated hydrogels, such as mesenchymal stem cells (MSCs), are capable of reducing inflammation and increasing the regenerative potential in several tissues. In addition, the use of MSC-derived nano-scale secretions (i.e., exosomes) has been promising. Exosomes originate from the multivesicular division of cells and have high therapeutic potential, yet neither self-replicate nor cause auto-immune reactions to the host. To maintain their biological activity and allow a controlled release, these paracrine factors can be encapsulated in biomaterials. Among the different types of biomaterials in which exosome infusion is exploited, hydrogels have proven to be the most user-friendly, economical, and accessible material. In this paper, we highlight the importance of MSCs and MSC-derived exosomes in tissue engineering and the different biomaterial strategies used in fabricating exosome-based biomaterials, to facilitate hard and soft tissue engineering.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".