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Record W2138836263 · doi:10.1080/0962935031000096962

Upregulation of expression of the chemokine receptor CCR5 by hydrogen peroxide in human monocytes

2003· article· en· W2138836263 on OpenAlexaff
Geneviève Lehoux, Christian Le Gouill, Jana Staňková, Marek Rola‐Pleszczynski

Bibliographic record

VenueMediators of Inflammation · 2003
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsChemokineHydrogen peroxideDownregulation and upregulationOxidative stressChemistryChemokine receptorReceptorMonocyteCell biologyMolecular biologyChemotaxisFlow cytometryC-C chemokine receptor type 6Gene expressionBiologyImmunologyBiochemistryGene

Abstract

fetched live from OpenAlex

The objective of this study was to test the hypothesis that an oxidative stress can serve as a signal to regulate the expression of CCR5. When human monocytes were exposed to graded concentration of hydrogen peroxide (H(2)O(2)), CCR5 mRNA levels increased maximally at 4 h of exposure to 200 microM of H(2)O(2) and decreased by 24 h of treatment. Pretreatment of monocytes with the NF-kappaB inhibitor BAY 11-8072 blocked the H(2)O(2)-induced augmentation of CCR5 mRNA expression, suggesting a role for this transcription factor in the regulation of CCR5 expression. CCR5 protein expression on the plasma membrane was also increased by treatment with H(2)O(2,) as assessed by flow cytometry. This was accompanied by enhanced responsiveness of H(2)O(2)-pretreated monocytes to the CCR5 ligand MIP-1beta in terms of chemotaxis and c-fos gene activation. Our results suggest that oxidative stress may indeed modulate the expression of chemokine receptors and thus contribute to regulation of the inflammatory process.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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

Citations16
Published2003
Admission routes1
Has abstractyes

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