A novel method to enhance the stability of alginate-poly-L-lysine-alginate microcapsules
Bibliographic record
Abstract
Implantation of microencapsulated recombinant cells is an alternative approach to gene therapy. These genetically-engineered cells enclosed in microcapsules to deliver therapeutic recombinant products have been effective in treating several murine models of human diseases. However, the most commonly used microcapsules fabricated from alginate ionically cross-linked with calcium suffer from loss of long-term mechanical stability. We now report on a method to improve their stability by introducing additional polymers to provide covalent linkages via photopolymerization. Vinyl monomers and a photoinitiator were allowed to diffuse into the initially formed calcium-alginate microcapsules. In situ photopolymerization in the presence of sodium acrylate and N-vinylpyrrolidone substantially enhanced their mechanical strength. After four months of storage in saline, > 70% of these capsules remained intact in the osmotic pressure test, while the un-modified alginate microcapsules totally disintegrated. Tests of their permeability to polyethylene glycol of different molecular weight and their ability to support cell survival showed that these properties remained unaffected by the photopolymerization. Hence, these microcapsules modified by adding a network of vinyl polymers are promising candidates to use for long-term delivery of recombinant gene products in this cell-based method of gene 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 | 0.001 |
| 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".