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Record W4297498192 · doi:10.1038/s42003-022-04005-4

Assessing the fitness of a dual-antiviral drug resistant human influenza virus in the ferret model

2022· article· en· W4297498192 on OpenAlexfundno aff
Harry Stannard, Edin Mifsud, Steffen Wildum, Sook Kwan Brown, Paulina Koszalka, Takao Shishido, Satoshi Kojima, Shinya Omoto, Keiko Baba, Klaus Kuhlbusch, Aeron C. Hurt, Ian Barr

Bibliographic record

VenueCommunications Biology · 2022
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
FundersInstitute of Infection and ImmunityDepartment of Health and Aged Care, Australian GovernmentAustralian GovernmentF. Hoffmann-La RocheRoche
KeywordsVirusVirologyNeuraminidaseTransmission (telecommunications)BiologyPandemicInfluenza A virusDrug resistanceNeuraminidase inhibitorMicrobiologyH5N1 genetic structureMedicineCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)Internal medicineDisease

Abstract

fetched live from OpenAlex

Influenza antivirals are important tools in our fight against annual influenza epidemics and future influenza pandemics. Combinations of antivirals may reduce the likelihood of drug resistance and improve clinical outcomes. Previously, two hospitalised immunocompromised influenza patients, who received a combination of a neuraminidase inhibitor and baloxavir marboxil, shed influenza viruses resistant to both drugs. Here-in, the replicative fitness of one of these A(H1N1)pdm09 virus isolates with dual resistance mutations (NA-H275Y and PA-I38T) was similar to wild type virus (WT) in vitro, but reduced in the upper respiratory tracts of challenged ferrets. The dual-mutant virus transmitted well between ferrets in an airborne transmission model, but was outcompeted by the WT when the two viruses were co-administered. These results indicate the dual-mutant virus had a moderate loss of viral fitness compared to the WT virus, suggesting that while person-to-person transmission of the dual-resistant virus may be possible, widespread community transmission is unlikely.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.286
GPT teacher head0.503
Teacher spread0.216 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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