Developing Citrus Germplasm Resistant to Asian Citrus Psyllid Using CRISPR/Cas9 Gene Editing Technology: Recent Advances and Challenges
Bibliographic record
Abstract
This study aims to explore recent advances and challenges in developing citrus germplasm resistant to the Asian citrus psyllid (ACP) using CRISPR/Cas9 gene editing technology.The focus is on identifying key genetic targets, evaluating the effectiveness of CRISPR/Cas9-mediated edits, and discussing the implications for sustainable citrus production.Several case studies demonstrate the potential of CRISPR/Cas9 to enhance resistance without compromising yield and fruit quality.Advances in CRISPR/Cas9 techniques, such as base and prime editing, have improved the precision and efficiency of gene editing in citrus.Additionally, field trials have validated the effectiveness of these edited plants in real-world conditions.The findings underscore the significant potential of CRISPR/Cas9 technology in developing ACP-resistant citrus germplasm.However, technical challenges, off-target effects, genetic stability, and regulatory and public acceptance issues remain.Continued research, interdisciplinary collaboration, and clear regulatory frameworks are essential to fully realize the benefits of CRISPR/Cas9 in citrus breeding.These efforts are crucial for ensuring the long-term sustainability and resilience of the citrus industry.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".