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Record W3198197731 · doi:10.1128/spectrum.00256-21

<i>In Silico</i>Analysis of Dengue Virus Serotype 2 Mutations Detected at the Intrahost Level in Patients with Different Clinical Outcomes

2021· article· en· W3198197731 on OpenAlexfundno aff
María C. Vázquez Torres, Ana L. M. Karl, Matheus Müller Pereira da Silva, Laurent E. Dardenne, Ana María Bispo de Filippis

Bibliographic record

VenueMicrobiology Spectrum · 2021
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsnot available
FundersAgence Nationale de la RechercheInternational Development Research Centre
KeywordsDengue feverBiologyDengue virusVirologyGeneticsArbovirusViral pathogenesisIn silicoEvolutionary biologyComputational biologyVirusGeneViral replication

Abstract

fetched live from OpenAlex

Previous evidence showed that intrahost genetic diversity in arboviruses may be linked to viral pathogenesis and that one or a few amino acid replacements within a single protein are enough to modify a biological feature of an RNA virus. To assess dengue virus serotype 2 determinants potentially involved in pathogenesis, we previously analyzed the intrahost genetic diversity of the virus in patients with different clinical outcomes and identified a set of 141 mutations that deserved further study. Thus, through a molecular modeling approach, we showed that disruptive variants were identified primarily among cases with mild dengue fever, while potential immune-escape variants were mainly associated with cases of greater severity. We believe that some of the variants pointed out in this study were attractive enough to be potentially considered in future intelligent designs of therapeutic or prophylactic compounds or the improvement of diagnostic tools. The present analysis provides new information about DENV-2 viral factors possibly involved in its pathogenesis within the human host.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.278
Teacher spread0.265 · 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 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

Citations9
Published2021
Admission routes1
Has abstractyes

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