Controlled Delivery of Human Cells by Temperature Responsive Microcapsules
Bibliographic record
Abstract
Cell therapy is one of the most promising areas within regenerative medicine. However, its full potential is limited by the rapid loss of introduced therapeutic cells before their full effects can be exploited, due in part to anoikis, and in part to the adverse environments often found within the pathologic tissues that the cells have been grafted into. Encapsulation of individual cells has been proposed as a means of increasing cell viability. In this study, we developed a facile, high throughput method for creating temperature responsive microcapsules comprising agarose, gelatin and fibrinogen for delivery and subsequent controlled release of cells. We verified the hypothesis that composite capsules combining agarose and gelatin, which possess different phase transition temperatures from solid to liquid, facilitated the destabilization of the capsules for cell release. Cell encapsulation and controlled release was demonstrated using human fibroblasts as model cells, as well as a therapeutically relevant cell line-human umbilical vein endothelial cells (HUVECs). While such temperature responsive cell microcapsules promise effective, controlled release of potential therapeutic cells at physiological temperatures, further work will be needed to augment the composition of the microcapsules and optimize the numbers of cells per capsule prior to clinical evaluation.
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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) | 0.000 | 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".