“Smart” Matrix Microneedle Patch Made of Self‐Crosslinkable and Multifunctional Polymers for Delivering Insulin On‐Demand
Bibliographic record
Abstract
A transdermal patch that delivers insulin at high glucose concentrations can offer tremendous advantages to ease the concern of safety and improve the quality of life for people with diabetes. Herein, a novel self-crosslinkable and glucose-responsive polymer-based microneedle patch (MN) is designed to deliver insulin at hyperglycemia. The microneedle patch is made of hyaluronic acid polymers functionalized with dopamine and 4-amino-3-fluorophenylboronic acid (AFBA) that can be quickly crosslinked upon mixing of the polymer solutions in the absence of any chemicalcrosslinking agents or organic solvents. The catechol groups in the dopamine (DA) units form covalent crosslinkages among themselves by auto-oxidation and dynamic crosslink with phenylboronic acid (PBA) via complexation. The reversible crosslinkages between catechol and boronate decrease with increasing glucose concentration leading to higher swelling and faster insulin release at hyperglycemia as compared to euglycemia. Such superior glucose-responsive properties are demonstrated by in vitro analyses and in vivo efficacy studies. The hydrogel polymers also preserve native structure and bioactivity of insulin, attributable to the interaction of hyaluronic acid (HA) with insulin molecules, as revealed by experiments and molecular dynamics simulations. The simplicity in the design and fabrication process, and glucose-responsiveness in insulin delivery impart the matrix microneedle (mMN) patch great potential for clinical translation.
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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".