CRISPR-Based Diagnostic for In-Field Detection of Plant Pathogens
Bibliographic record
Abstract
Plant fungal pathogens are an increasingly common and costly threat to agricultural yields, resulting in wasted produce and the frequent use of fungicides. To enable more targeted use of fungicides, methods for detecting and identifying plant fungal infections in a rapid, low-cost, decentralized, and power-free manner are needed. CRISPR-based diagnostics have recently emerged as an effective tool for sensitive and specific pathogen detection in resource-limited settings. We present the preliminary designs of a CRISPR-based diagnostic for the soil-borne fungus Colletotrichum coccodes. Bioinformatics analysis led to the identification of four gRNA candidates targeting the ITS 1 and ITS 2 regions of C. coccodes for high specificity. The most promising gRNA was further screened and detection of DNA concentrations down to 500 pM without amplification are shown. This diagnostic for C. coccodes could be used in agricultural settings for rapid and cost-effective detection of fungal pathogens to enable high-yield sustainable agricultural practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".