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Record W2036035346 · doi:10.1038/sj.clpt.6100420

Heterologous Prime–Boost Strategy to Immunize Very Young Infants against Measles: Pre-clinical Studies in Rhesus Macaques

2007· article· en· W2036035346 on OpenAlexaff
Marcela F. Pasetti, Aldo Arturo Reséndiz‐Albor, Karina Ramírez, Robert L. Stout, Mark Papania, Robert J. Adams, Fernando P. Polack, Brian J. Ward, David S. Burt, Sophie Chabot, Jeffrey B. Ulmer, Eileen M. Barry, Myron M. Levine

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2007
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsGlaxoSmithKline (Canada)McGill University
FundersCenters for Disease Control and PreventionJohns Hopkins UniversityBill and Melinda Gates Foundation
KeywordsMeaslesMedicineVirologyViremiaImmunologyVaccinationRubellaMeasles vaccineDNA vaccinationImmunizationMeasles virusVirusAntibody

Abstract

fetched live from OpenAlex

Infants in developing countries are at high risk of developing severe clinical measles if they become infected during the "window of vulnerability" (age 4-9 months), when declining maternal antibodies do not protect against wild virus, yet impede successful immunization by attenuated measles vaccine. We developed two Sindbis replicon-based DNA vaccines expressing measles virus hemagglutinin and fusion protein with the goal of priming young infants to respond safely and effectively to subsequent boosting with attenuated measles vaccine. Intradermal prime with DNA vaccines by needle-free injection followed by aerosol or parenteral boost with licensed measles vaccine was well tolerated by juvenile and young infant rhesus macaques, and protected against clinical measles and viremia on wild-type virus challenge. A proteosome-measles vaccine administered alone (three doses) or as a boost following DNA vaccine priming was also safe and protective. These promising results pave the way for clinical trials to assess this prime-boost strategy.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.246
GPT teacher head0.550
Teacher spread0.304 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations30
Published2007
Admission routes1
Has abstractyes

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