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Record W2948604111 · doi:10.3390/v11060514

Mast Cells and Natural Killer Cells—A Potentially Critical Interaction

2019· review· en· W2948604111 on OpenAlexafffund
Liliana Portales‐Cervantes, Bassel Dawod, Jean S. Marshall

Bibliographic record

VenueViruses · 2019
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsBiologyInnate immune systemImmunologyImmune systemEffectorLymphokine-activated killer cellCell biologyMast cellPriming (agriculture)Natural killer cellInnate lymphoid cellInterleukin 21Cytotoxic T cellT cellIn vitro

Abstract

fetched live from OpenAlex

Natural killer (NK) cells play critical roles in host defense against infectious agents or neoplastic cells. NK cells provide a rapid innate immune response including the killing of target cells without the need for priming. However, activated NK cells can show improved effector functions. Mast cells are also critical for early host defense against a variety of pathogens and are predominately located at mucosal surfaces and close to blood vessels. Our group has recently shown that virus-infected mast cells selectively recruit NK cells and positively modulate their functions through mechanisms dependent on soluble mediators, such as interferons. Here, we review the possible consequences of this interaction in both host defense and pathologies involving NK cell and mast cell activation.

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 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 score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.013

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 head0.042
GPT teacher head0.323
Teacher spread0.281 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations30
Published2019
Admission routes2
Has abstractyes

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