Characterizing an injectable alginate hydrogel as a co‐encapsulating system for beta cells and curcumin in type 1 diabetes therapy
Bibliographic record
Abstract
Abstract Encapsulating insulin‐secreting cells within biocompatible hydrogels is a tissue engineering approach for type 1 diabetes treatment. Macroencapsulation is preferred over other encapsulation methods due to its easy implantation and retrieval. However, this treatment has challenges, including the need for invasive surgery for macrocapsule implantation and host response, resulting in fibrotic overgrowth around the implant and subsequent failure of the graft. Herein, an injectable alginate hydrogel, fabricated by different concentrations of as a retarding agent, was applied to avoid surgery. According to characterization tests, hydrogel made by 0.3 M showed a higher swelling ratio and diffusion coefficient, appropriate for the diffusion of nutrients, oxygen, and insulin. It was also stiffer with a lower swelling rate, demonstrating more robust elastic and solid‐like behaviour, suitable for cell attachment. Its small pore size (133.04 ± 53.28 μm), additionally, was essential for inhibiting immunoglobulins penetration. Thus, this hydrogel was used for RIN‐5F cells encapsulation and demonstrated good biocompatibility (>88% viability), and maintained the insulin‐releasing function of cells. Afterward, we co‐encapsulated beta cells with curcumin as an anti‐inflammatory drug and investigated the effect of this agent on cell behaviour in vitro. It was indicated that curcumin did not adversely affect viability and insulin secretion.
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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".