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Record W4403092346 · doi:10.1126/science.adn4955

A multivalent mRNA-LNP vaccine protects against <i>Clostridioides difficile</i> infection

2024· article· en· W4403092346 on OpenAlexaff
Mohamad‐Gabriel Alameh, Alexa Semon, Nile U. Bayard, Yi‐Gen Pan, Garima Dwivedi, James J. Knox, Rochelle C. Glover, Paula C. Rangel, Ceylan Tanes, Kyle Bittinger, Qianxuan She, Haitao Hu, Srinivasa Reddy Bonam, Jeffrey R. Maslanka, Paul J. Planet, Ahmed M. Moustafa, Benjamin Davis, Anik Chevrier, Mitchell Beattie, Houping Ni, Gabrielle S. Blizard, Emma E. Furth, Robert H. Mach, Marc Lavertu, Mark A. Sellmyer, Ying K. Tam, Michael C. Abt, Drew Weissman, Joseph P. Zackular

Bibliographic record

VenueScience · 2024
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsAcuitas Therapeutics (Canada)Polytechnique Montréal
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthChildren's Hospital of Philadelphia
KeywordsClostridioidesVirologyMicrobiologyMessenger RNABiologyGeneGenetics

Abstract

fetched live from OpenAlex

Clostridioides difficile infection (CDI) is an urgent public health threat with limited preventative options. In this work, we developed a messenger RNA (mRNA)–lipid nanoparticle (LNP) vaccine targeting C. difficile toxins and virulence factors. This multivalent vaccine elicited robust and long-lived systemic and mucosal antigen-specific humoral and cellular immune responses across animal models, independent of changes to the intestinal microbiota. Vaccination protected mice from lethal CDI in both primary and recurrent infection models, and inclusion of non-toxin cellular and spore antigens improved decolonization of toxigenic C. difficile from the gastrointestinal tract. Our studies demonstrate mRNA-LNP vaccine technology as a promising platform for the development of novel C. difficile therapeutics with potential for limiting acute disease and promoting bacterial decolonization.

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)0.001
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 score0.072
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.323
Teacher spread0.299 · 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

Citations73
Published2024
Admission routes1
Has abstractyes

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