Antimicrobial Peptides Protect Coho Salmon from <i>Vibrio anguillarum</i> Infections
Bibliographic record
Abstract
Fish losses from infectious diseases are a significant problem in aquaculture worldwide. Therefore, we investigated the ability of cationic antimicrobial peptides to protect against infection caused by the fish pathogen Vibrio anguillarum. To identify effective peptides for fish, the MICs of certain antimicrobial peptides against fish pathogens were determined in vitro. Two of the most effective antimicrobial peptides, CEME, a cecropin-melittin hybrid peptide, and pleurocidin amide, a C-terminally amidated form of the natural flounder peptide, were selected for in vivo studies. A single intraperitoneal injection of CEME did not affect mortality rates in juvenile coho salmon infected with V. anguillarum, the causative agent of vibriosis. Therefore, the peptides were delivered continuously using miniosmotic pumps placed in the peritoneal cavity. Twelve days after pump implantation, the fish received intraperitoneal injections of V. anguillarum at a dose that would kill 50 to 90% of the population. Fish receiving 200 microg of CEME per day survived longer and had significantly lower accumulated mortalities (13%) than the control groups (50 to 58%). Fish receiving pleurocidin amide at 250 microg per day also survived longer and had significantly lower accumulated mortalities (5%) than the control groups (67 to 75%). This clearly shows the potential for antimicrobial peptides to protect fish against infections and indicates that the strategy of overexpressing the peptides in transgenic fish may provide a method of decreasing bacterial disease problems.
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) | 0.001 | 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".