Laser-engineered dissolving microneedle arrays for protein delivery: potential for enhanced intradermal vaccination
Bibliographic record
Abstract
OBJECTIVES: We aimed to highlight the utility of novel dissolving microneedle (MN)-based delivery systems for enhanced transdermal protein delivery. Vaccination remains the most accepted and effective approach in offering protection from infectious diseases. In recent years, much interest has focused on the possibility of using minimally invasive MN technologies to replace conventional hypodermic vaccine injections. METHODS: The focus of this study was exploitation of dissolving MN array devices fabricated from 20% w/w poly(methyl vinyl ether/maleic acid) using a micromoulding technique, for the facilitated delivery of a model antigen, ovalbumin (OVA). KEY FINDINGS: A series of in-vitro and in-vivo experiments were designed to demonstrate that MN arrays loaded with OVA penetrated the stratum corneum and delivered their payload systemically. The latter was evidenced by the activation of both humoral and cellular inflammatory responses in mice, indicated by the production of immunoglobulins (IgG, IgG1, IgG2a) and inflammatory cytokines, specifically interferon-gamma and interleukin-4. Importantly, the structural integrity of the OVA following incorporation into the MN arrays was maintained. CONCLUSION: While enhanced manufacturing strategies are required to improve delivery efficiency and reduce waste, dissolving MN are a promising candidate for 'reduced-risk' vaccination and protein delivery strategies.
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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".