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Record W2100861375 · doi:10.1093/infdis/jis340

Critical Role of Natural Killer Cells in Lung Immunopathology During Influenza Infection in Mice

2012· article· en· W2100861375 on OpenAlexafffund
Mohamed Faizal Abdul-Careem, M. Firoz Mian, Geoffry Yue, Amy Gillgrass, Meghan J. Chenoweth, Nicole G. Barra, Marianne V. Chew, Tiffany Chan, Amal Al‐Garawi, Manel Jordana, Ali A. Ashkar

Bibliographic record

VenueThe Journal of Infectious Diseases · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsMcMaster University Medical Centre
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsImmunologyBronchoalveolar lavageImmune systemBiologyCytokineProinflammatory cytokineInflammationInfluenza A virusCD8Interleukin 12LungInterleukinNatural killer T cellNatural killer cellCytotoxic T cellVirusMedicineIn vitro

Abstract

fetched live from OpenAlex

Influenza viral infection results in excessive pulmonary inflammation that has been linked to the damage caused by immune responses and viral replication. The multifunctional cytokine interleukin (IL-15), influences the proliferation and maintenance of immune cells such as CD8(+) T cells and natural killer (NK) cells. Here we show that IL-15(-/-) mice are protected from lethal influenza infection. Irrespective of the mouse strains, the protection observed was linked to the lack of NK cells. Increased survival in the IL-15(-/-) or NK1.1(+) cell-depleted wild-type mice was associated with significantly lower lung lesions as well as decreased mononuclear cells and neutrophils in the airway lumen. Levels of interleukin 10 were significantly higher and levels of proinflammatory cytokines, including interleukin 6 and interleukin 12, were significantly lower in the bronchoalveolar lavage fluid from IL-15(-/-) and NK1.1(+) cell-depleted wild-type mice than in that from control mice. Our data suggest that NK cells significantly augment pulmonary inflammation, contributing to the pathogenesis of influenza infection.

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 score0.469
Threshold uncertainty score0.376

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 integrity0.0000.001
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 head0.005
GPT teacher head0.246
Teacher spread0.242 · 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

Citations127
Published2012
Admission routes2
Has abstractyes

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