Novel nano-in-micro fabrication technique of diclofenac nanoparticles loaded microneedle patches for localised and systemic drug delivery
Bibliographic record
Abstract
Diclofenac, a nonsteroidal anti-inflammatory drug, is commonly prescribed for managing osteoarthritis, rheumatoid arthritis, and post-surgical pain. However, oral administration of diclofenac often leads to adverse effects. This study introduces an innovative nano-in-micro approach to create diclofenac nanoparticle-loaded microneedle patches aimed at localised, sustained pain relief, circumventing the drawbacks of oral delivery. The nanoparticles were produced via wet-milling, achieving an average size of 200 nm, and then incorporated into microneedle patches. These patches showed improved skin penetration in ex vivo tests using Franz-cell setups compared to traditional diclofenac formulations. In vivo tests on rats revealed that the nanoparticle-loaded microneedle patches allowed for quick drug uptake and prolonged release, maintaining drug levels in tissues for up to 72 h. With a systemic bioavailability of 57 %, these patches prove to be an effective means of transdermal drug delivery. This study highlights the potential of this novel microneedle delivery system in enhancing the treatment of chronic pain with reduced systemic side effects.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".