Osmotic pressure test: A simple, quantitative method to assess the mechanical stability of alginate microcapsules
Bibliographic record
Abstract
Implantation of microencapsulated, nonautologous cells and tissues is an effective method to deliver therapeutic proteins in vivo. Its success depends on the maintenance of the immunoisolating barrier provided by the microcapsule. Thus, one goal in the development of this technology is to create mechanically stable microcapsules. We have developed an osmotic pressure test to quantify the strength of microcapsules by exposing alginate microcapsules to a graded series of hypotonic solutions and quantifying the percentage of broken microcapsules. The test was validated by confirming the relative strengths of different types of alginate capsules, previously known from implantation in dogs to have differing mechanical stability in vivo. Thus, solid alginate microcapsules crosslinked with Ba(2+) were shown to be stronger than those crosslinked with Ca(2+), which in turn were shown to be stronger than the corresponding hollow alginate microcapsules. The incorporation of cells was demonstrated to reduce the mechanical stability of the microcapsules significantly. Hence, this test provides a simple and quantitative method for rapidly determining the strength of a large number of microcapsules. Thus, it is suitable for monitoring the mechanical stability of various types of microcapsules, predicting the performance of microcapsules in vivo, and for quality control of microcapsules during scale-up productions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".