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Record W3176925377 · doi:10.1139/cjc-2021-0117

Itaconate: an antimicrobial metabolite of macrophages

2021· article· en· W3176925377 on OpenAlexafffundvenue
Dustin Duncan, Karine Auclair

Bibliographic record

VenueCanadian Journal of Chemistry · 2021
Typearticle
Languageen
FieldMedicine
TopicPneumocystis jirovecii pneumonia detection and treatment
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsAntimicrobialChemistryPseudomonas aeruginosaMicrobiologySalmonella entericaBacteriaIsocitrate lyaseYersinia pseudotuberculosisEnzymeImmune systemIn vitroMetabolitePathogenAntibioticsBiochemistryEscherichia coliBiologyGlyoxylate cycleVirulenceGene

Abstract

fetched live from OpenAlex

Itaconate is a conjugated 1,4-dicarboxylate produced by macrophages. This small molecule has recently received increasing attention due to its role in modulating the immune response of macrophages upon exposure to pathogens. Itaconate has also been proposed to play an antimicrobial function; however, this has not been explored as intensively. Consistent with the latter, itaconate is known to show antibacterial activity in vitro and was reported to inhibit isocitrate lyase, an enzyme required for survival of bacterial pathogens in mammalian systems. Recent studies have revealed bacterial growth inhibition under biologically relevant conditions. In addition, an antimicrobial role for itaconate is substantiated by the high concentration of itaconate found in bacteria-containing vacuoles, and by the production of itaconate-degrading enzymes in pathogens such as Salmonella enterica ser. Typhimurium, Pseudomonas aeruginosa, and Yersinia pestis. This review describes the current state of literature in understanding the role of itaconate as an antimicrobial agent in host–pathogen interactions.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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 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

Citations7
Published2021
Admission routes3
Has abstractyes

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Same venueCanadian Journal of ChemistrySame topicPneumocystis jirovecii pneumonia detection and treatmentFrench-language works237,207