Polymeric 3D-Printed Microneedle Arrays for Non-Transdermal Drug Delivery and Diagnostics
Bibliographic record
Abstract
Microneedle arrays (MNAs) are becoming increasingly popular due to their ease of use and effectiveness in drug delivery and diagnostic applications. Improvements in three-dimensional (3D) printing techniques have made it possible to fabricate MNAs with high precision, intricate designs, and customizable properties, expanding their potential in medical applications. While most studies have focused on transdermal applications, non-transdermal uses remain relatively underexplored. This review summarizes recent developments in 3D-printed MNAs intended for non-transdermal drug delivery and diagnostic purposes. It includes a literature review of studies published in the past ten years, organized by the target delivery site-such as the brain and central nervous system (CNS), oral cavity, eyes, gastrointestinal (GI) tract, and cardiovascular and reproductive systems, among other emerging areas. The findings show that 3D-printed MNAs are more adaptable than skin-based delivery, opening up exciting new possibilities for use in a variety of organs and systems. To guarantee the effective incorporation of polymeric non-transdermal MNAs into clinical practice, additional research is necessary to address current issues with materials, manufacturing processes, and regulatory approval.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".