Identification of Disease Resistance Genes and CRISPR-Based Ge-nome Editing in Channa spp.
Bibliographic record
Abstract
This study analyzed the hazards and immune response mechanisms of common diseases of snakehead fish in recent years (such as nocardia, Aeromonas hydrophila , viral hemorrhagic septicemia, etc.), and summarized the mining methods and functional research progress of key genes for disease resistance of snakehead fish, including screening of immune genes such as IL-17 and TRAF through whole genome scanning and transcriptomics. At the same time, the application status and advantages of CRISPR/Cas9 gene editing technology in aquaculture were discussed, such as efficient site-directed mutagenesis and introduction of exogenous antimicrobial peptide genes to enhance fish disease resistance. Through case analysis of the successful experience of disease-resistant gene editing in related fish (such as Atlantic salmon and catfish), this study prospected the potential path and results of disease-resistant gene editing breeding of snakehead fish, and discussed its ecological and ethical impacts (such as off-target effects, food safety and public acceptance, etc.), which is of great significance to improving aquaculture production and disease prevention and control, and also provides the latest theoretical basis and practical reference for disease-resistant breeding of snakehead fish.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".