Assessing mesh size and diffusion of alginate bioinks: A crucial factor for successful bioprinting functional pancreatic islets
Bibliographic record
Abstract
Three-dimensional (3D) bioprinting has been utilised for the encapsulation of pancreatic islets for potentially treating type 1 diabetes. A crucial factor in selecting a cell compatible bioink, that maintains islet functionality, is the mesh size and diffusion capacity of the bioink. In this study, we present a screening strategy for alginate hydrogel formulations in three-dimensional bioprinting, utilizing the fluorescent recovery after photobleaching (FRAP) method and measuring the mesh size of the hydrogels. Subsequently, the 1.5 % alginate formulation that had been selected was used to bioprint the INS1E cell line, primary rat islets, or human islets. It was demonstrated that cell viability and functionality were maintained in all cell sources. This was evidenced by the observation that bioprinted pancreatic islets exhibited a response to physiological glucose levels. The present study indicates that both FRAP and hydrogel mesh size measurements are effective tools for predicting the diffusion of hormones through a hydrogel. These measures should therefore be incorporated into future screenings of hydrogel compositions for the 3D bioprinting of islets.
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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 | 0.001 |
| 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) | 0.001 | 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".