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Record W2127186127 · doi:10.1016/j.virol.2014.07.013

Impaired heterologous immunity in aged ferrets during sequential influenza A H1N1 infection

2014· article· en· W2127186127 on OpenAlexaff
Stéphane G. Paquette, Stephen S. H. Huang, David W. Banner, Luoling Xu, Alberto J. León, Alyson A. Kelvin, David J. Kelvin

Bibliographic record

VenueVirology · 2014
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsToronto General HospitalToronto Rehabilitation InstituteUniversity of TorontoDiscovery CentreUniversity Health Network
FundersNational Institute of Allergy and Infectious DiseasesU.S. Public Health ServiceNational Institutes of HealthShantou University Medical College
KeywordsBiologyHeterologousImmune systemImmunologyHemagglutinin (influenza)ImmunityVirologyPopulationInfluenza A virusHerd immunityAntibodyVirusVaccinationMedicineGene

Abstract

fetched live from OpenAlex

The major burden of influenza morbidity resides within the elderly population. The challenge managing influenza-associated illness in the elderly is the decline of immune function, where mechanisms leading to immunological senescence have not been elucidated. To better represent the immune environment, we investigated clinical morbidity and immune function during sequential homologous and heterologous H1N1 influenza infection in an aged ferret model. Our findings demonstrated experimentally that aged ferrets had significant morbidity during monosubtypic heterologous 2° challenge with significant weight loss and respiratory symptoms. Furthermore, increased clinical morbidity was associated with slower and shorter hemagglutinin antibody generation and attenuated type 1 T-cell gene responses in peripheral blood. These results revealed dampened immune activation during sequential influenza infection in aged ferrets. With the presence of an aged model, dissecting clinical morbidity, viral dynamics and immune response during influenza infection will aid the development of future prophylactics such as age specific influenza vaccines.

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)0.001
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 score0.403
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.069
GPT teacher head0.370
Teacher spread0.300 · 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

Citations49
Published2014
Admission routes1
Has abstractyes

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