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Record W2370170892 · doi:10.1189/jlb.71.3.458

Distinct fates of monocytes and T cells directly activated by<i>Pseudomonas aeruginosa</i>exoenzyme S

2002· article· en· W2370170892 on OpenAlexaff
Slava Epelman, G. Gregory Neely, Ling Ling, Mark Gjomarkaj, Elisabetta Pace, Mario Melis, Donald E. Woods, Christopher H. Mody

Bibliographic record

VenueJournal of Leukocyte Biology · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsExoenzymeBiologyMicrobiologyPseudomonas aeruginosaSecretionChemotaxisMonocyteCell biologyVirulence factorVirulenceImmunologyBacteriaBiochemistryEnzymeReceptor

Abstract

fetched live from OpenAlex

Gram-negative infections can cause overwhelming inflammatory responses. Although factors other than LPS are clearly involved, these factors and their mechanisms of action have been poorly defined. During studies of LPS-independent inflammatory responses of the gram-negative pathogen Pseudomonas aeruginosa, an important virulence factor (exoenzyme S) was shown to be a potent mitogen for T cells. The current work demonstrates that exoenzyme S selectively induced transcription and secretion of biologically active cytokines and chemokines (chemotactic for neutrophils and T cells) from monocytes. Exoenzyme S stimulated highly purified monocytes independent of T cells. In addition, exoenzyme S stimulated T cells directly; neither T-cell activation (CD69) nor apoptosis (hypodiploidy) required the presence of monocytes. However, T-cell activation was enhanced via a noncontact-dependent mechanism as a result of the secretion of TNF-alpha and IL-6. This study identifies a unique property of a gram-negative-derived microbial product capable of activating multiple cell types and suggests a mechanism by which exoenzyme S contributes to the immunopathogenesis of cystic fibrosis and sepsis in patients infected with P. aeruginosa.

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 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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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.

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

Citations25
Published2002
Admission routes1
Has abstractyes

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