Rational Design of Rapidly Separating Dissolving Microneedles for Precise Drug Delivery by Balancing the Mechanical Performance and Disintegration Rate
Bibliographic record
Abstract
The precise delivery of traditional dissolving microneedles (TDMNs) is often limited by the incomplete insertion due to the skin deformation, and the topical irritation is inevitable after long application, which ultimately results in compromised therapeutic efficacy. The aim of this study is to develop a rapidly separating dissolving microneedles (RSDMNs) system to achieve precise drug delivery. Therapeutic molecules are concentrated in the needle tip, while the blank separating part allows it to counteract skin indentation and rapidly separate from the base part. For rational design of an ideal separating part, and the molecular interactions between polymer and sugar are explored to make a good balance between mechanical performance and disintegration rate. The optimal RSDMNs can rapidly disintegrate in the mimic skin within 30 s, and the generated micropores in the skin reseal quickly. The ex vivo drug permeation of RSDMNs is significantly higher than that of TDMNs due to the complete needle imbed aided by the separating part. Furthermore, RSDMNs exhibit excellent in vivo anti-inflammation effect by remarkably down regulating the expression of TNF-α, IL-1β, and IL-6. In conclusion, the RSDMNs can reach precise drug delivery in a short time, which are more reliable for the self-administration strategy in the future.
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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) | 0.000 | 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".