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NK Cell‐mediated Destruction of Influenza A Virus‐infected Peripheral but not Central Neurones

2007· article· en· W2011163887 on OpenAlexaff
Eva Backström, HG Ljunggren, Krister Kristensson

Bibliographic record

VenueScandinavian Journal of Immunology · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsTerry Fox Research InstituteBC Cancer Agency
FundersVetenskapsrådet
KeywordsBiologyImmune systemImmunologyNeuriteCell biologyInterferonCentral nervous systemNeuroscienceIn vitro

Abstract

fetched live from OpenAlex

Peripheral neurones have the potential to transmit infectious agents to the central nervous system (CNS). This raises the possibility of existing host defence mechanisms that may prevent such spread. Natural killer (NK) cells can target infected cells, and by this ability serve to limit spread of infection prior to the development of adaptive immune responses. To address directly if NK cells can target infected peripheral neurones, we examined the expression of NK cell-activating ligands and susceptibility to NK cell-mediated cytolytic effects in ex vivo cultures of mouse peripheral dorsal root ganglia (DRG) neurones prior to and after infection with a neurotropic strain of influenza A virus, WSN/33. In infected DRG cultures, retinoic acid early inducible gene-1 (RAE-1) transcripts were induced and exposure to interleukin (IL)-2-activated NK cells resulted in a total destruction of neurites. Studies on cultures from interferon (IFN)-alpha/betaR-deficient mice suggest that the infection engages an IFN-alpha/beta-dependent signalling pathway to induce RAE-1 transcripts. In contrast, induction of RAE-1 transcripts or NK cell-mediated neurite destructions was not observed in central hippocampal neurones. This reveals distinct properties between peripheral DRG and central hippocampal neurones with respect to the ability to signal for immune destruction following 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 categoriesInsufficient payload (model declined to judge)
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 score0.187
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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.

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

Citations12
Published2007
Admission routes1
Has abstractyes

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