Mechanical replication of natural fever enhances protection against Aeromonas veronii infection in a teleost fish
Bibliographic record
Abstract
There is a long-standing debate on the attributes of temperature for fish health. We recently showed that thermoregulatory programs exerted through natural behavioural fever drive molecular and cellular responses that contribute to pathogen clearance, inflammation control, and tissue repair. These offered a mechanistic basis for the survival advantage conferred through fever. Herein, we show the attributes of mechanical replication of this fever response. Central to our approach was consideration of both, the maximal temperatures naturally selected by fish after infection, as well as the dynamics of thermal changes induced through this response. Coarse replication of the febrile thermal program as well as shorter truncated thermal schedules offered immune-regulatory capacity. Most notably, these promoted induction of acute inflammation and significant enhancements to pathogen clearance. However, the coarse protocols tested only partially recapitulated enhancements to induction and control of tissue repair. Our findings highlight a promising new alternative to combat infections in fish using a natural, drug-free, sustainable approach.
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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".