Protection of Artemia from vibriosis by heat shock and heat shock proteins
Bibliographic record
Abstract
Disease imposes an important constraint on aquaculture and in this context vibriosis brings about massive mortalities in many commercial species, ranging from fish to shrimp, with resultant heavy monetary losses. During my work the application of heat shock proteins (Hsps) as an approach to disease control in aquaculture was explored, with gnotobiotic brine shrimp Artemia larvae as the model organism. The thesis contains several findings, featuring the interesting observation that a non-lethal heat shock at 37°C for 30 min followed by a 6 h recovery period and a combined hypothermic/hyperthermic shock with temperature reduction from 28°C to 4°C for 1 h followed by incubation at 37°C for 30 min and a 6 h recovery at ambient temperature optimally enhanced resistance of gnotobiotic Artemia larvae against pathogenic vibrios and induced Hsp70 maximally. The resulting two-fold increase in survival of larvae in concert with stress protein synthesis suggested that endogenous Hsp70 functions in protection. Likewise, feeding gnotobiotic Artemia with E. coli cells over-producing prokaryotic heat shock proteins, particularly the 70kDa DnaK, promotes resistance to infection by a pathogenic bacterium. This study demonstrates for the first time that Hsp70 homologs can induce a protective effect towards a pathogen when it is supplied via the feed to the host, perhaps via immune enhancement. Importantly, this work reveals the potential application of Hsps as a way to enhance shrimp resistance and as a novel microbial countermeasure to infection. These techniques represent efficient strategies to control Vibrio infection in brine shrimp and may serve as alternatives to antibiotic use in aquaculture. Moreover, we provide new evidence for relationships between Hsps and immunity in Artemia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".