Encapsulating therapeutic cells in RGD-modified agarose microcapsules
Bibliographic record
Abstract
Current cell-based strategies for repairing damaged tissue often show limited efficacy due to low cell retention at the site of injury. Encapsulation of cells within hydrogel microcapsules demonstrably increases cell retention but benefits can be limited due to premature cell escape from the hydrogel microcapsules and subsequent clearance from the targeted tissue. We propose a method of encapsulating cells in agarose microcapsules that have been modified to increase cell retention by providing cell attachment domains within the agarose hydrogel allowing cells to adhere to the microcapsules. We covalently modified agarose with the addition of the cell adhesion peptide, RGD (arginine, glycine, aspartic acid). We then used a microfluidic platform to encapsulate single cells within 50 μm agarose microcapsules. We tracked encapsulated cells for cell viability, egress from microcapsules and attachment to microcapsules at 2 h, 24 h, and 48 h after encapsulation. Many encapsulated cells eventually egress their microcapsule. Those that were encapsulated using RGD-modified agarose adhered to the outer surface of the microcapsule following egress. NIH 3T3 cells showed nearly 45% of egressed cells attached to the outside of RGD modified agarose microcapsules, while minimal cellular adhesion was observed when using unmodified agarose. Similarly, human umbilical vein endothelial cells had up to 33% of egressed cells attached and explant-derived cardiac cells showed up to 20% attachment with the presence of RGD binding domains within the agarose microcapsules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".