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Record W6948193803 · doi:10.5061/dryad.q573n5tr8

Data from: Self-amplifying RNA generated with the modified nucleotides 5-methylcytidine and 5-methyluridine mediate strong expression and immunogenicity in vivo

2024· dataset· en· W6948193803 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2024
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicSubterranean biodiversity and taxonomy
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsRNANucleotideImmunogenicityGene expressionMessenger RNAGuanosineGeneIntronImmune systemUridine

Abstract

fetched live from OpenAlex

When utilized in therapeutic applications, synthetic self-amplifying RNA can lead to higher and more sustained expression than standard messenger RNA. This feature is particularly important for gene replacement therapy applications where prolonged expression could reduce the dose and frequency of treatments. The inclusion of modified nucleotides in synthetic non-amplifying mRNA has been shown to increase RNA stability, reduce immune activation and enhance gene expression. Preclinical and clinical studies with self-amplifying RNA (saRNA) have so far exclusively relied on RNA containing the canonical nucleotides adenosine, cytidine, guanosine and uridine. For the first time, we show that non-canonical nucleotides, such as m5C and m5U, are sufficiently compatible with a replicon derived from Venezuelan equine encephalitis alphavirus mediating protein translation in vitro, while those containing m1ψ in place of uridine show no detectable expression. When administered in vivo, saRNA generated with m5C or m5U mediate sustained gene expression of the luciferase reporter gene with those incorporating m5U appearing to lead to more prolonged expression. Finally, distinct antigen-specific humoral and cellular immune responses were induced by modified saRNA encoding the model antigen ovalbumin. The use of modified nucleotides with saRNA-based platforms could enhance their potential to be used effectively in a variety of applications.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.231
Teacher spread0.174 · 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
GenreDataset

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

Citations0
Published2024
Admission routes1
Has abstractyes

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