MétaCan
Menu
Back to cohort
Record W3119222651 · doi:10.1080/14760584.2020.1874928

Microneedle Systems for Vaccine Delivery: the story so far

2020· review· en· W3119222651 on OpenAlexaff
Md Kamal Hossain, Taksim Ahmed, Prabhat Bhusal, Robhash Kusam Subedi, Iman Salahshoori, M. Soltani, Majid Hassanzadeganroudsari

Bibliographic record

VenueExpert Review of Vaccines · 2020
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsMedicineDelivery systemVirologyPharmacology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.745
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.131
GPT teacher head0.481
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations53
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueExpert Review of VaccinesSame topicAdvancements in Transdermal Drug DeliveryFrench-language works237,207