Glucose-Responsive Implantable Polymeric Microdevices for "Smart" Insulin Therapy of Diabetes
Bibliographic record
Abstract
Diabetes mellitus is a chronic illness manifested by improper blood glucose management, affecting over 350 million worldwide. As a result, all type 1 patients and roughly 20% of type 2 patients require exogenous insulin therapy to survive. Typically, daily multiple injections are taken to maintain normal glucose levels in response glucose spikes from meals. However, patient compliance and dosing accuracy can fluctuate with variation in meals, exercise, glucose metabolism or stress, leading to poor clinical outcomes. A `smart', closed-loop insulin delivery system providing on-demand release kinetics responding to circulating glucose levels would be a boon for diabetes patients, replacing constant self monitoring and insulin. This thesis focuses on the development of a novel, `smart' insulin microdevice that can provide on-demand insulin release in response to blood glucose levels. \nIn the early stage, the feasibility of integrating a composite membrane with pH-responsive nanoparticles embedded in ethylcellulose membrane to provide pH-responsive in vitro release was examined and confirmed using a model drug, vitamin B12. In the second microdevice, glucose oxidase for generating pH signals from glucose oxidation, catalase and manganese dioxide nanoparticles, as peroxide scavengers, were used in a bioinorganic, albumin-based membrane cross-linked with a polydimethylsiloxane (PDMS) grid-microdevice system. This prototype device demonstrated insulin release in response to glucose levels in vitro and regulating plasma glucose in type 1 diabetic rats when implanted intraperitoneally. \nAdvancement allowing for subcutaneous implantation and improved biocompatibility was achieved with surface modification of PDMS microdevices grafted with activated 20 kDa polyethylene glycol (PEG) chains, dramatically reducing immune response and local inflammation. When implanted subcutaneously in diabetic rats, glucose-responsive insulin delivery microdevices showed short and long-term efficacy up to an 18 day period. Finally, to improve insulin stability within microdevice reservoirs, an in situ gelling zinc-insulin formulation was designed. High concentration insulin gel complexed with zinc provided physical and chemical stability against thermal denaturation over a 30 day period. Long-term stability of the zinc-insulin gel formulation shows potential for sustained release application, providing low-level, basal insulin release. These combined technologies present significant progress towards the goal of an `artificial pancreas' to combat diabetes through `smart' insulin therapy.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".