Nanotechnology‐Enabled Closed Loop Insulin Delivery Device: In Vitro and In Vivo Evaluation of Glucose‐Regulated Insulin Release for Diabetes Control
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Recently, a new multifunctional, bio‐inorganic nanocomposite membrane with the ability to self‐regulate the release of insulin in response to blood glucose (BG) levels was reported. Herein, the application of this material as part of a small, implantable, closed‐loop insulin delivery device designed to continuously monitor BG concentrations and regulate insulin release is proposed. The insulin delivery device consists of a nanocomposite glucose‐responsive plug covalently bound to an insulin reservoir made of surface‐modified silicone. The plug is prepared with crosslinked bovine serum albumin (BSA) and enzymes (glucose oxidase (GOx) and catalase (CAT)), pH‐responsive hydrogel nanoparticles, and multifunctional MnO 2 nanoparticles. The plug functions both as a glucose sensor and controlled delivery unit to release higher rates of insulin from the reservoir in response to hyperglycemic BG levels and basal insulin rates at normal BG concentration. The surfaces of the device are modified by silanization followed by PEGylation to ensure its safety and biocompatibility and the stability of encased insulin. Our results show that insulin release can be modulated in vitro in response to glucose concentrations. In vivo experiments show that the glycemia of diabetic rats can be controlled with implantation of the prototype device. The glucose‐responsiveness of the device is also demonstrated by rapid drop in BG level after challenging diabetic rats with bolus injection of glucose solution. In addition, it is demonstrated that surface PEGylation of the device is necessary for reducing the immune response of the host to the implanted foreign object and maintaining insulin stability and bioactivity. With this molecular architecture and the bio‐inorganic nanocomposite plug, the device has the ability to maintain normal BG levels in diabetic rats.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 it