An immune regulatory 3D-printed alginate-pectin construct for immunoisolation of insulin producing β-cells
Bibliographic record
Abstract
Different bioinks have been used to produce cell-laden alginate-based hydrogel constructs for cell replacement therapy but some of these approaches suffer from issues with print quality, long-term mechanical instability, and bioincompatibility. In this study, new alginate-based bioinks were developed to produce cell-laden grid-shaped hydrogel constructs with stable integrity and immunomodulating capacity. Integrity and printability were improved by including the co-block-polymer Pluronic F127 in alginate solutions. To reduce inflammatory responses, pectin with a low degree of methylation was included and tested for inhibition of Toll-Like Receptor 2/1 (TLR2/1) dimerization and activation and tissue responses under the skin of mice. The viscoelastic properties of alginate-Pluronic constructs were unaffected by pectin incorporation. The tested pectin protected printed insulin-producing MIN6 cells from inflammatory stress as evidenced by higher numbers of surviving cells within the pectin-containing construct following exposure to a cocktail of the pro-inflammatory cytokines namely, IL-1β, IFN-γ, and TNF-α. The results suggested that the cell-laden construct bioprinted with pectin-alginate-Pluronic bioink reduced tissue responses via inhibiting TLR2/1 and support insulin-producing β-cell survival under inflammatory stress. Our study provides a potential novel strategy to improve long-term survival of pancreatic islet grafts for Type 1 Diabetes (T1D) treatment.
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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".