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Record W4317358862 · doi:10.4049/jimmunol.2200599

Leukotrienes in Innate Immunity: Still Underappreciated after All These Years?

2023· review· en· W4317358862 on OpenAlexafffund
C. Henrique Serezani, Maziar Divangahi, Marc Peters‐Golden

Bibliographic record

VenueThe Journal of Immunology · 2023
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsChristie (Canada)McGill University Health Centre
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsInnate immune systemBiologyNeuroinflammationImmune systemImmunologyPhagocyteNeuroscienceSignal transductionArachidonate 5-lipoxygenaseImmunityComputational biologyInflammationCell biologyArachidonic acid

Abstract

fetched live from OpenAlex

Leukotrienes (LTs) are lipid mediators derived from the 5-lipoxygenase pathway of arachidonate metabolism. Though best known for their role in asthma, they have broad actions that touch on virtually every aspect of mammalian biology. In a Brief Review published in the journal in 2005, we presented the existing evidence supporting a role for LTs in host defense. In this updated Brief Review, we focus on selected advances since then. We detail new insights into mechanisms and regulation of LT biosynthesis; the protective roles of LTs in the host response to diverse classes of pathogens, with an emphasis on viruses, including SARS-CoV-2; the phagocyte signal transduction mechanisms by which LTs exert their antimicrobial actions; the capacity for overexuberant LT production to promote tissue damage; and roles of LTs in the noninfectious immune-relevant conditions neuroinflammation and cancer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.066
GPT teacher head0.352
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2023
Admission routes2
Has abstractyes

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