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Record W2911171305 · doi:10.1080/22221751.2018.1560234

Diverse biological characteristics and varied virulence of H7N9 from Wave 5

2019· article· en· W2911171305 on OpenAlexaff
Linlin Bao, Yuhai Bi, Gary Wong, Wenbao Qi, Fengdi Li, Qi Lv, Liang Wang, Fei Liu, Yang Yang, Cheng Zhang, William J. Liu, Chuansong Quan, Weixin Jia, Yingxia Liu, Wenjun Liu, Ming Liao, George F. Gao, Chuan Qin

Bibliographic record

VenueEmerging Microbes & Infections · 2019
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversité Laval
FundersYouth Innovation Promotion Association of the Chinese Academy of SciencesNational Natural Science Foundation of China
KeywordsVirulenceBiologyVirologyTransmissibility (structural dynamics)VirusTransmission (telecommunications)PathogenicityInfluenza A virus subtype H5N1MicrobiologyGeneGenetics

Abstract

fetched live from OpenAlex

There was a substantial increase with infections of H7N9 avian influenza virus (AIV) in humans during Wave 5 (2016-2017). To investigate whether H7N9 had become more infectious/transmissible and pathogenic overall, we characterized the receptor binding and experimentally infected ferrets with highly pathogenic (HP)- and low pathogenic (LP)-H7N9 isolates selected from Wave 5, and compared their pathogenicity and transmissibility with a Wave 1 isolate from 2013. Studies show that A/Anhui/1/2013 (LP) and A/Chicken/Heyuan/16876/2016 (HP) were highly virulent in ferrets, A/Guangdong/Th008/2017 (HP) and A/Chicken/Huizhou/HZ-3/2017 (HP) had moderate virulence and A/Shenzhen/Th001/2016 (LP) was of low virulence in ferrets. Transmission was observed only in ferrets infected with A/Anhui/1/2013 and A/Chicken/Heyuan/16876/2016, consistent with the idea that sicker ferrets had a higher probability to transmit virus to naive animals. Given the Varied virulence and transmissibility observed in circulating H7N9 viruses from Wave 5, we conclude that the current public health risk of H7N9 has not substantially increased compared to 2013 and the circulating viruses are quite diverse.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.058
GPT teacher head0.327
Teacher spread0.269 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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