Investigation of encapsulation of pancreatic beta cells and curcumin within alginate microcapsules
Bibliographic record
Abstract
Abstract Cell encapsulation is an ideal approach for the replacement of pancreatic function in Type 1 diabetes. Poor biocompatibility of microcapsules generates an inflammatory response in the implantation site and induces fibrosis infiltration, which causes microencapsulated cell death and graft failure. To prevent inflammation after implantation, composite microcapsules that exhibit anti‐inflammatory properties were designed. This study is about encapsulating beta cells and curcumin within 1.5% alginate by the jet‐breaking regime of the syringe pump. The microcapsules’ size distribution and rate of the alginate solution were characterized to find uniform particles. Micro‐size particles were attained at a rate of 25 mL/min. Uniform spherical microcapsules (200–300 μm) were created in large amounts in a short period. Microcapsule breakage was less than 3% during 7 days, which demonstrated the stability of the encapsulation method. Insulin secretion and cell viability assays were performed 1, 3, and 7 days after microencapsulation by glucose‐stimulated insulin secretion (GSIS) and 3‐[4,5‐dimethylthiazol‐2‐yl]‐2,5 diphenyl tetrazolium bromide (MTT) assays. No significant differences in the amount of insulin secretion and beta cell viability were observed among free cells, alginate microcapsules, and curcumin‐alginate microcapsules during 7 days ( p > 0.05). Therefore, the curcumin and alginate membrane did not show any harmful impacts on the function and survival of the beta cells.
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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 | 0.000 |
| 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".