Wire bonded solid metal microneedles: a versatile platform technology for transdermal drug delivery and biosensing
Bibliographic record
Abstract
Microneedles are emerging as a promising technology for transdermal drug delivery and biosensing due to their ability to penetrate the skin’s upper layers without interacting with pain-sensitive deeper nervous tissue. This characteristic makes them an attractive alternative to hypodermic needles for various therapeutic and diagnostic applications. However, traditional fabrication methods for microneedles often require complex, multi-step processes that are costly and unsuitable for scalable manufacturing, particularly for metal microneedles, which are ideal for biosensing applications due to their excellent electrical properties. Our group has previously developed a novel low cost and high throughput method of fabricating solid metal microneedles using a modified wire bonding process. In this work, we explore the dual potential of these wire-bonded microneedles for drug delivery and biosensing. For biosensing, we functionalized the microneedles with the glucose oxidase enzyme immobilized within a porous electro-polymer coating. We demonstrated the ability to detect physiologically relevant glucose concentrations through benchtop chronoamperometric measurements in prepared glucose solutions and glucose-soaked porcine skin tissue. Furthermore, we developed a miniaturized hardware interface for these microneedle arrays that will facilitate future development of a wearable microneedle-based glucose sensing system. For drug delivery, we successfully demonstrated that wire bonded solid metal microneedles can be coated with a chitosan hydrogel loaded with the chemotherapeutic agent doxorubicin, thus verifying their potential use as a drug delivery platform. These results highlight the versatility of wire bonded metal microneedles for both drug delivery and biosensing, demonstrating their potential as a scalable platform technology.
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) | 0.000 | 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 | 0.001 |
| 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 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".