3D printed hollow microneedles: the latest innovation in drug delivery
Bibliographic record
Abstract
INTRODUCTION: Hollow microneedles (HMNs) offer a minimally invasive and highly efficient method for transdermal drug administration, overcoming the limitations of traditional delivery systems. AREAS COVERED: This review focuses on recent advancements in 3D-printed HMNs, highlighting their transformative potential in drug delivery applications. The integration of cutting-edge 3D printing technologies, such as stereolithography (SLA), digital light processing (DLP), and two-photon polymerization (2PP), has enabled the fabrication of complex, precise, and customizable microneedles (MNs). These innovations facilitate patient-specific applications, enhance drug bioavailability, and provide unparalleled control over dosage and delivery. Advances in biocompatible and biodegradable materials have further improved the safety and functionality of HMNs. Applications range from insulin delivery to biomarker sensing and theranostic systems, showcasing their versatility. EXPERT OPINION: 3D-printed HMNs are set to play an important role in improving personalized medicine and precision healthcare. By addressing fabrication and design issues, and using new materials, these devices are expected to change drug delivery systems and help develop new therapeutic and diagnostic platforms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".