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Record W1888350770 · doi:10.1128/aac.01885-15

Assessment of Antiviral Properties of Peramivir against H7N9 Avian Influenza Virus in an Experimental Mouse Model

2015· article· en· W1888350770 on OpenAlexafffund
Amber Farooqui, Linxi Huang, Suwu Wu, Yingmu Cai, Min Su, Pengzhou Lin, Weihong Chen, Xibin Fang, Li Zhang, Yisu Liu, Tiansheng Zeng, Stéphane G. Paquette, Adnan Khan, Alyson A. Kelvin, David J. Kelvin

Bibliographic record

VenueAntimicrobial Agents and Chemotherapy · 2015
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of TorontoToronto General HospitalUniversity Health Network
FundersInstitute of Infection and ImmunityLi Ka Shing Foundation
KeywordsVirusVirologyNeuraminidaseOseltamivirNeuraminidase inhibitorInfluenza A virusMedicineViral replicationImmunologyBiologyDiseaseCoronavirus disease 2019 (COVID-19)Internal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The H7N9 influenza virus causes a severe form of disease in humans. Neuraminidase inhibitors, including oral oseltamivir and injectable peramivir, are the first choices of antiviral treatment for such cases; however, the clinical efficacy of these drugs is questionable. Animal experimental models are essential for understanding the viral replication kinetics under the selective pressure of antiviral agents. This study demonstrates the antiviral activity of peramivir in a mouse model of H7N9 avian influenza virus infection. The data show that repeated administration of peramivir at 30 mg/kg of body weight successfully eradicated the virus from the respiratory tract and extrapulmonary tissues during the acute response, prevented clinical signs of the disease, including neuropathy, and eventually protected mice against lethal H7N9 influenza virus infection. Early treatment with peramivir was found to be associated with better disease outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.119
GPT teacher head0.389
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2015
Admission routes2
Has abstractyes

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