Microneedle Systems for Vaccine Delivery: the story so far
Bibliographic record
Abstract
INTRODUCTION: Vaccine delivery via a microneedle (MN) system has been identified as a potential alternative to conventional vaccine delivery. MN can be self-administered, is pain-free and is capable of producing superior immunogenicity. Over the last few decades, significant research has been carried out in this area, and this review aims to provide a comprehensive picture on the progress of this delivery platform. AREAS COVERED: This review highlights the potential role of skin as a vaccine delivery route using a microneedle system, examines recent advancements in microneedle fabrication techniques, and provides an update on potential preclinical and clinical studies on vaccine delivery through microneedle systems against various infectious diseases. Articles for the review study were searched electronically in PubMed, Google, Google Scholar, and Science Direct using specific keywords to cover the scope of the article. The advanced search strategy was employed to identify the most relevant articles. EXPERT OPINION: A significant number of MN mediated vaccine candidates have shown promising results in preclinical and clinical trials. The recent emergence of cleanroom free, 3D or additive manufacturing of MN systems and stability, together with the dose-sparing capacity of the Nanopatch® system, have made this platform, commercially, highly lucrative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".