Photosensitizer to the Rescue: In Planta and Field Application of Photodynamic Inactivation Against Plant-Pathogenic Bacteria
Bibliographic record
Abstract
Control of plant pathogens using chemical and synthetic pesticides raises a major safety concern for humans and the environment. Despite the ongoing exploration of sustainable alternative methods, management practices for pathogens, especially bacteria, have remained almost unchanged over decades, whereby long-term uses of copper and antibiotics has led to widespread bacterial resistance in the field. Antimicrobial photodynamic inactivation (aPDI) of bacteria is emerging as an alternative strategy to combat resistant plant pathogens. aPDI utilizes light-sensitive molecules (photosensitizers) that upon illumination produce reactive oxygen species able to kill pathogens. Here we explore the potential of an anionic semisynthetic water-soluble derivative of chlorophyl (Sodium Magnesium Chlorophyllin: Mg-chl), as an antibacterial agent in planta, by simulating processes naturally occurring in the field. Mg-chl in combination with Na2EDTA (cell wall permeabilizing agent) was able to effectively inhibit Pseudomonas syringae pv. tomato DC3000 in vitro and in planta in both tomato and N. benthamiana. Notably, Mg-chl in combination with Na2EDTA and the common surfactant Morwet D-400 significantly reduced Xanthomonas hortorum pv. gardneri and Xanthomonas fragarie, respectively, in a commercial greenhouse trial against bacterial spot disease in tomato and in field experiments against angular leaf spot disease in strawberries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".