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Redox-active metals in commercial preparations of lipopolysaccharide: implications for studies of cellular responses to bacterial products

2003· article· en· W2079930867 on OpenAlexafffund
John L. Brubacher, Stephanie J. DeWitte‐Orr, Joanna R. Zorzitto, Richard C. Playle, Niels C. Bols

Bibliographic record

VenueCellular Microbiology · 2003
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaWilfrid Laurier University
KeywordsLipopolysaccharideBiologySuperoxide dismutaseReactive oxygen speciesCell biologyNitric oxideCatalaseCD14MacrophageBiochemistryOxidative stressReceptorImmunologyIn vitro

Abstract

fetched live from OpenAlex

The mechanisms by which lipopolysaccharide (LPS) activates cells have been the subject of intense investigation for many years. Whereas much information on this process has been collected for mammalian species, little is known about the signalling path-ways operative in other animals. One general mode of cellular activation that has been recently pro-posed for pathways independent of the primary mammalian LPS receptor, CD14, involves reactive oxygen species (ROS) as intermediates in LPS-induced signalling pathways. Therefore, we used 2',7'-dichlorodihydrofluorescein, a fluorogenic probe of redox activity, to examine LPS-induced oxidative responses of a macrophage-like cell line from the rainbow trout, RTS11. Lipopolysaccharide dose-dependently increased oxidation of this probe by RTS11 cells, and a variety of other cell lines. This process was inhibited by catalase, superoxide dismutase and NG-methylarginine citrate, an inhibitor of nitric oxide synthases, suggesting the involvement of a diverse assortment of cellular ROS. More careful dissection of this phenomenon led us to conclude that the increase in oxidation was, in fact, due almost entirely to metals, particularly copper, in some LPS preparations, which is something to consider when experimenting with LPS.

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)0.001
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 score0.059
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.043
GPT teacher head0.309
Teacher spread0.266 · 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

Citations7
Published2003
Admission routes2
Has abstractyes

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