Effect of poly‐<scp>L</scp>‐lysine coating on macrophage activation by alginate‐based microcapsules: Assessment using a new <i>in vitro</i> method
Bibliographic record
Abstract
The characteristics of the microcapsule surface, which interacts directly with the host macrophages, may have a role in the biocompatibility of alginate-poly-L-lysine (PLL)-alginate (APA) microcapsule. The objectives of the study were: 1) to develop and validate a simple, rapid, and sensitive in vitro method for assessing microcapsule biocompatibility, based on microcapsule coincubation with macrophages and measurement, by reverse transcriptase-polymerase chain reaction, of cytokine mRNA expression, and 2) to evaluate the effect of alginate purification and PLL coating on macrophage activation. The mRNA expression of tumor necrosis factor-alpha and interleukin-1beta was significantly higher when macrophages were coincubated with beads made with nonpurified compared with purified alginate (p<0.01, p<0.05, respectively) and negative control (p<0.001) or with APA microcapsules compared with non-PLL-coated alginate beads and negative control (p<0.001). The mRNA expression of interleukin-6 differed significantly only when APA microcapsules were compared with a negative control (p<0.05). These results confirm that alginate purification improves microcapsule biocompatibility, and suggest that PLL is not completely covered and/or neutralized by the second alginate incubation and thus has a role in the host macrophage activation. The assay is sensitive to both alginate contaminants and microcapsule surface characteristics and may be a useful tool for the development of biocompatible microcapsules.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".