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Feeding <i>Artemia franciscana</i> (Kellogg) larvae with bacterial heat shock protein, protects from <i>Vibrio campbellii</i> infection

2009· article· en· W2109783154 on OpenAlexaff
Yeong Yik Sung, M F Ashame, S. Chen, Thomas H. MacRae, Patrick Sorgeloos, Peter Bossier

Bibliographic record

VenueJournal of Fish Diseases · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBiologyMicrobiologyVibrioBacteriaHeat shock proteinEscherichia coliLarvaHsp70VirologyEcologyBiochemistryGeneticsGene

Abstract

fetched live from OpenAlex

Among their numerous physiological effects, heat shock proteins (Hsps) are potent immunomodulators, a characteristic reflecting their potential as therapeutic agents and which led to their application in combating infection. As an example, the up-regulation of endogenous Hsp70 in the branchiopod crustacean Artemia franciscana (Kellogg) is concurrent with shielding against bacterial infection. To better understand this protective mechanism, gnotobiotic Artemia were fed with Escherichia coli treated to over-produce different prokaryotic Hsps. This was shown to increase larval resistance to experimental Vibrio campbellii exposure. Immunoprobing of Western blots showed that the enhanced resistance to V. campbellii correlated with DnaK production in E coli. A definitive role for DnaK was then demonstrated by feeding Artemia larvae with transformed bacteria over-producing only this protein, although other Hsps such as DnaJ and grpE also provided tolerance against Vibrio infection. Feeding of bacteria synthesizing selected Hsps is therefore suggested as an alternative to antibiotic use as a means of enhancing resistance of Artemia larvae to bacterial infection, which may have potential applications in aquaculture.

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 score0.086
Threshold uncertainty score0.853

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 head0.007
GPT teacher head0.238
Teacher spread0.230 · 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

Citations37
Published2009
Admission routes1
Has abstractyes

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