Expression of Hsp70, Igf1, and Three Oxidative Stress Biomarkers in Response to Handling and Salt Treatment at Different Water Temperatures in Yellow Perch, Perca flavescens
Bibliographic record
Abstract
Stress is a major factor that causes diseases and mortality in the aquaculture industry. The goal was to analyze the expression of stress-related biomarkers in response to different stressors in yellow perch, which is an important aquaculture candidate in North America and highly sensitive to handling in captivity. Three fish groups were established, each having four replicates, and subjected to water temperatures of 14, 20, and 26°C and acute handling stress was performed followed by a salt treatment for 144h at a salinity of 5 ppt. Serum and hepatic mRNA levels of heat shock protein (hsp70), insulin-like growth factor 1 (Igf1), glutathione peroxidase (Gpx), superoxide dismutase 1 (Sod1), and glutathione reductase (Gsr) were quantified at seven times interval over 144 h using ELISA and RT-qPCR. Handling stress caused a significant down-regulation in Hsp70, Gpx, Sod1, and Gsr at a water temperature of 20°C compared to 14 and 26°C. Igf1 was significantly upregulated at 20°C and down-regulated at 14 and 26°C. Salt treatment had a transient reverse effect on the targeted biomarkers in all groups at 72 h, then caused an upregulation after 144 h, compared to the control groups. The data showed a negative strong regulatory linear relationship between igf1 with hsp70 and anti-oxidative gene expressions. These findings could provide valuable new insights into the stress responses that affect fish health and could be used to monitor the stress.
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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".