Glucose‐Responsive Composite Microneedle Patch for Hypoglycemia‐Triggered Delivery of Native Glucagon
Bibliographic record
Abstract
Insulin-dependent patients with diabetes mellitus require multiple daily injections of exogenous insulin to combat hyperglycemia. However, administration of excess insulin can lead to hypoglycemia, a life-threatening condition characterized by abnormally low blood glucose levels (BGLs). To prevent hypoglycemia associated with intensive insulin therapy, a "smart" composite microneedle (cMN) patch is developed, which releases native glucagon at low glucose levels. The cMN patch is composed of a photo-crosslinked methacrylated hyaluronic acid (MeHA) microneedle array with embedded multifunctional microgels. The microgels incorporate zwitterionic moieties that stabilize loaded glucagon and phenylboronic acid moieties that provide glucose-dependent volume change to facilitate glucagon release. Hypoglycemia-triggered release of structurally unchanged glucagon from the cMN patch is demonstrated in vitro and in a rat model of type 1 diabetes (T1D). Transdermal application of the patch prevented insulin-induced hypoglycemia in the diabetic rats. This work is the first demonstration of a glucose-responsive glucagon-delivery MN patch for the prevention of hypoglycemia, which has a tremendous potential to reduce the dangers of intensive insulin therapy and improve the quality of life of patients with diabetes and their caregivers.
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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) | 0.000 | 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".