Effect of Cell Density of a Methacrylic Acid-Based Hydrogel Implant on Embedded Islet Function and Viability
Bibliographic record
Abstract
Subcutaneous delivery of islets in a methacrylic acid-based hydrogel may offer a functional cure for type 1 diabetes. Here we show in mice that the hydrogel is able to provide sufficient vasculature to support islet function and viability, when islets are used at a low islet volume fraction (i.e., cell density). The Krogh cylinder model was used to mathematically estimate the effect of implant volume, for a fixed islet dose (600 islet equivalents [IEQ]), on the minimum vessel density required to maintain sufficient pO 2 within the graft. Modeling suggested that 200 μL implants would have low enough islet densities and enough vessels to have islets remain viable, but that 50 μL implants would not; this was confirmed experimentally through measurement of glucose level in streptozotocin-induced diabetic severe combined immunodeficiency disease (SCID/bg) mice, comparing 200 and 50 μL implants, both with 600 IEQ. Vessel densities were ∼20–30 vessels/mm 2 independent of implant volume and vessels were sufficient to increase subcutaneous oxygen tension, as measured with microcapsules containing oxygen sensitive material (a platinum [Pt] porphyrin); both these results were determined without cells. These results are useful in thinking about the scale-up of this system to humans: to maintain a low islet density (∼0.5%), many more islets will require attention to the subcutaneous implant configuration to satisfy the oxygen needs of the 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) | 0.001 | 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".