Biomedical Applications of Polymeric Microneedles for Transdermal Therapeutic Delivery and Diagnosis: Current Status and Future Perspectives
Bibliographic record
Abstract
Abstract Transdermal drug delivery is a crucial extension of drug administration routes and has been widely acknowledged as an alternative way to traditional oral administration and subcutaneous injections due to the advantages such as increased dosage efficacy, decreased systemic side effect, and improved patient compliance. The past few decades have witnessed biomedical applications of microneedles in various cutting‐edge fields. Microneedles are needles with micron‐scale length which can pierce the epidermis of the skin in a minimally invasive manner for transdermal drug delivery. Compared with inorganic and metal microneedles, polymeric microneedles have attracted more attention because of their superior biocompatibility, nontoxicity, and biodegradability. In this review, the state‐of‐art and future biomedical applications of polymeric microneedles are summarized. First of all, a brief introduction to the polymeric microneedles, including types of polymeric microneedles and methods for the fabrication is included. Then the biomedical applications of polymeric microneedles in transdermal drug delivery and diagnosis are summarized in detail. Finally, discussions on the current limitations and future perspectives of polymeric microneedles are provided.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".