Enhancing Therapeutic Insulin Transport from Macroencapsulated Islets Using Sub-Minute Pressure at Physiological Levels
Bibliographic record
Abstract
Cadaveric islet and stem cell-derived transplantations hold promise as treatments for type 1 diabetes. To tackle the issue of immunocompatibility, numerous cellular macroencapsulation techniques have been developed that utilize diffusion to transport insulin across an immunoisolating barrier. However, despite several devices progressing to human clinical trials, none have successfully managed to attain physiologic glucose control or insulin independence. Based on empirical evidence, macroencapsulation methods with multilayered, high islet surface density are incompatible with homeostatic, on-demand insulin delivery and physiologic glucose regulation, when reliant solely on diffusion. An additional driving force is essential to overcome the distance limit of diffusion. In this study, we present both theoretical proof and experimental validation that applying pressure at levels comparable to physiological diastolic blood pressure significantly enhances insulin flux across immunoisolation membranes-increasing it by nearly three orders of magnitude. This significant enhancement in transport rate allows for precise, sub-minute regulation of both bolus and basal insulin delivery. By incorporating this technique with a pump-based extravascular system, we demonstrate the ability to rapidly reduce glucose levels in diabetic rodent models, effectively replicating the timescale and therapeutic effect of subcutaneous insulin injection or infusion. This advance provides a potential path towards achieving insulin independence with islet macroencapsulation. One Sentence Summary: Towards improved glucose control, applying sub-minute pressure at physiological levels enhances therapeutic insulin transport from macroencapsulated 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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".