A Conductive Hydrogel Microneedle‐Based Assay Integrating PEDOT:PSS and Ag‐Pt Nanoparticles for Real‐Time, Enzyme‐Less, and Electrochemical Sensing of Glucose
Bibliographic record
Abstract
Continuous glucose meters (CGMs) have tremendously boosted diabetes care by emancipating millions of diabetic patients' need for repeated self-testing by pricking their fingers every few hours. However, CGMs still suffer from major deficiencies regarding accuracy, precision, and stability. This is mainly due to their dependency on an enzymatic detection mechanism. Here a low-cost hydrogel microneedle (HMN)-CGM assay fabricated using swellable dopamine (DA)-hyaluronic acid (HA) hydrogel for glucose interrogation in dermal interstitial fluid (ISF) is introduced. Platinum and silver nanoparticles are synthesized within the 3D porous hydrogel scaffolds for nonenzymatic electrochemical sensing of the glucose. Incorporation of a highly water dispersible conductive polymer, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) enhances the electrical properties of HMN array, making the patch suitable as the working electrode of the sensor. The in vitro and ex vivo characterization of this newly developed HMN patch is fully studied. The performance of the HMN-CGM for real-time measurement of glucose is also shown using a rat model of type 1 diabetes. The device introduces the first HMN-based assay for tracking important disease biomarkers and expect to pave the way for next generation of polymeric-based sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".