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Record W4385740534 · doi:10.3390/pharmaceutics15082114

Non-Invasive Vaccines: Challenges in Formulation and Vaccine Adjuvants

2023· review· en· W4385740534 on OpenAlexafffund
Sumin Han, P Lee, Hyo‐Jick Choi

Bibliographic record

VenuePharmaceutics · 2023
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImmunogenicityMedicineCommercializationRisk analysis (engineering)Intensive care medicineImmunologyBusinessImmune systemMarketing

Abstract

fetched live from OpenAlex

Given the limitations of conventional invasive vaccines, such as the requirement for a cold chain system and trained personnel, needle-based injuries, and limited immunogenicity, non-invasive vaccines have gained significant attention. Although numerous approaches for formulating and administrating non-invasive vaccines have emerged, each of them faces its own challenges associated with vaccine bioavailability, toxicity, and other issues. To overcome such limitations, researchers have created novel supplementary materials and delivery systems. The goal of this review article is to provide vaccine formulation researchers with the most up-to-date information on vaccine formulation and the immunological mechanisms available, to identify the technical challenges associated with the commercialization of non-invasive vaccines, and to guide future research and development efforts.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.175
GPT teacher head0.401
Teacher spread0.227 · 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 designOther design
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

Citations10
Published2023
Admission routes2
Has abstractyes

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