CRISPR-Cas9 Mediated Gene Editing in Tilapia: Enhancing Growth Rates and Disease Resistance
Bibliographic record
Abstract
CRISPR-Cas9 mediated gene editing has emerged as a transformative tool in aquaculture, offering precise genetic modifications to improve key traits in fish species such as tilapia. This study explores the potential of CRISPR-Cas9 technology to enhance growth rates and increase disease resistance in tilapia, a widely farmed species crucial to global food security. By targeting specific genes associated with growth and immune responses, CRISPR-Cas9 enables the rapid development of superior strains with stable and heritable traits. Case studies demonstrate successful gene editing applications that result in improved growth performance and enhanced disease resistance, thus reducing the need for antibiotics and supporting more sustainable aquaculture practices. However, challenges remain, including off-target effects, regulatory hurdles, and public acceptance. Ecological concerns, such as gene flow to wild populations, also warrant further investigation. Despite these challenges, CRISPR-Cas9 shows promise in transforming tilapia breeding programs by improving productivity and sustainability. As the technology advances and regulatory frameworks evolve, it is poised to have a long-lasting impact on the aquaculture industry.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".