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Mechanisms of Natural Killer Cell Evasion Through Viral Adaptation

2020· review· en· W3020012373 on OpenAlex
Mathieu Mancini, Silvia M. Vidal

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnnual Review of Immunology · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsBiologyImmune systemInnate immune systemVirusImmunologyDengue virusVirologyEvasion (ethics)Cell biology

Abstract

fetched live from OpenAlex

The continuous interactions between host and pathogens during their coevolution have shaped both the immune system and the countermeasures used by pathogens. Natural killer (NK) cells are innate lymphocytes that are considered central players in the antiviral response. Not only do they express a variety of inhibitory and activating receptors to discriminate and eliminate target cells but they can also produce immunoregulatory cytokines to alert the immune system. Reciprocally, several unrelated viruses including cytomegalovirus, human immunodeficiency virus, influenza virus, and dengue virus have evolved a multitude of mechanisms to evade NK cell function, such as the targeting of pathways for NK cell receptors and their ligands, apoptosis, and cytokine-mediated signaling. The studies discussed in this article provide further insights into the antiviral function of NK cells and the pathways involved, their constituent proteins, and ways in which they could be manipulated for host benefit.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.021
GPT teacher head0.293
Teacher spread0.272 · 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