Evaluation of alginate purification methods: Effect on polyphenol, endotoxin, and protein contamination
Bibliographic record
Abstract
Alginate, a polysaccharide extracted from brown seaweed, is widely used for the microencapsulation of islets of Langerhans, allowing their transplantation without immunosuppression. This natural polymer is known to be largely contaminated. The implantation of islets encapsulated using unpurified alginate leads to the development of fibrotic cell overgrowth around the microcapsules and normalization of the blood glucose is restricted to a very short period if it is achieved at all. Several research groups have developed their own purification method and obtained relatively good results. No comparative evaluation of the efficiencies of these methods has been published. We conducted an evaluative study of five different alginate preparations: a pharmaceutical-grade alginate in its raw state, the same alginate after purification according to three different published methods, and a commercially available purified alginate. The results showed that all purification methods reduced the amounts of known contaminants, that is, polyphenols, endotoxins, and proteins, although with varying efficiencies. Increased viscosity of alginate solutions was observed after purification of the alginates. Despite a general efficiency in decreasing contamination levels, all of the purified alginates contained relatively high residual amounts of protein contaminants. Because proteins may be immunogenic, these residual proteins may have a role in persisting microcapsule immunogenicity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".