Non-Lethal Heat Shock Increased Hsp70 and Immune Protein Transcripts but Not Vibrio Tolerance in the White-Leg Shrimp
Bibliographic record
Abstract
Non-lethal heat shock boosts bacterial and viral disease tolerance in shrimp, possibly due to increases in endogenous heat shock protein 70 (Hsp70) and/or immune proteins. To further understand the mechanisms protecting shrimp against infection, Hsp70 and the mRNAs encoding the immune-related proteins prophenoloxidase (proPO), peroxinectin, penaeidin, crustin and hemocyanin were studied in post-larvae of the white-leg shrimp Litopenaeus vannamei, following a non-lethal heat shock. As indicated by RT-qPCR, a 30 min abrupt heat shock increased Hsp70 mRNA in comparison to non-heated animals. Immunoprobing of western blots and quantification by ELISA revealed that Hsp70 production after heat shock was correlated with enhanced Hsp70 mRNA. proPO and hemocyanin mRNA levels were augmented, whereas peroxinectin and crustin mRNA levels were unchanged following non-lethal heat shock. Penaeidin mRNA was decreased by all heat shock treatments. Thirty min abrupt heat shock failed to improve survival of post-larvae in a standardized challenge test with Vibrio harveyi, indicating that under the conditions of this study, L. vannamei tolerance to Vibrio infection was influenced neither by Hsp70 accumulation nor the changes in the immune-related proteins, observations dissimilar to other shrimp species examined.
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 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) | 0.002 | 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".