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Record W4395074677 · doi:10.1038/s41467-024-47457-4

A comprehensive synthetic library of poly-N-acetyl glucosamines enabled vaccine against lethal challenges of Staphylococcus aureus

2024· article· en· W4395074677 on OpenAlexfundno aff
Zibin Tan, Weizhun Yang, Nicholas A. O’Brien, Xingling Pan, Sherif Ramadan, Terence L. Marsh, Neal D. Hammer, Colette Cywes‐Bentley, Mariana Vinacur, Gerald B. Pier, Jeffrey C. Gildersleeve, Xuefei Huang

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldChemistry
TopicCarbohydrate Chemistry and Synthesis
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesCollege of Engineering, Michigan State UniversityNational Cancer InstituteU.S. Department of Health and Human ServicesNational Institutes of HealthUniversity of AlbertaMichigan State University
KeywordsStaphylococcus aureusEpitopeMicrobiologyVirologyConjugateVaccinationMedicineBiologyAntigenBacteriaImmunologyGenetics

Abstract

fetched live from OpenAlex

Poly-β-(1-6)-N-acetylglucosamine (PNAG) is an important vaccine target, expressed on many pathogens. A critical hurdle in developing PNAG based vaccine is that the impacts of the number and the position of free amine vs N-acetylation on its antigenicity are not well understood. In this work, a divergent strategy is developed to synthesize a comprehensive library of 32 PNAG pentasaccharides. This library enables the identification of PNAG sequences with specific patterns of free amines as epitopes for vaccines against Staphylococcus aureus (S. aureus), an important human pathogen. Active vaccination with the conjugate of discovered PNAG epitope with mutant bacteriophage Qβ as a vaccine carrier as well as passive vaccination with diluted rabbit antisera provides mice with near complete protection against infections by S. aureus including methicillin-resistant S. aureus (MRSA). Thus, the comprehensive PNAG pentasaccharide library is an exciting tool to empower the design of next generation vaccines.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.021
GPT teacher head0.265
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations16
Published2024
Admission routes1
Has abstractyes

Explore more

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