Breaking the Rebellion: Photodynamic Inactivation against Erwinia amylovora Resistant to Streptomycin
Bibliographic record
Abstract
Global crop production depends on strategies to counteract the ever-increasing spread of plant pathogens. Antibiotics are often used for large-scale treatments. As a result, Erwinia amylovora, causal agent of the contagious fire blight disease, has already evolved resistance to streptomycin (Sm). Photodynamic Inactivation (PDI) of microorganisms has been introduced as innovative method for plant protection. The aim of this study is to demonstrate that E. amylovora resistant to Sm (E. amylovoraSmR) can be killed by PDI. Two photosensitizers, the synthetic B17-0024, and the natural derived anionic sodium magnesium chlorophyllin (Chl) with cell-wall-permeabilizing agents are compared in terms of their photo-killing efficiency in liquid culture with or without 100 µg/mL Sm. In vitro experiments were performed at photosensitizer concentrations of 1, 10 or 100 µM and 5 or 30 min incubation in the dark, followed by illumination at 395 nm (radiant exposure 26.6 J/cm2). The highest inactivation of seven log steps was achieved at 100 µM B17-0024 after 30 min incubation. Shorter incubation (5 min), likely to represent field conditions, reduced the photo-killing to 5 log steps. Chlorophyllin at 100 µM in combination with 1.2% polyaspartic acid (PASA) reduced the number of bacteria by 6 log steps. While PASA itself caused some light independent toxicity, an antibacterial effect (3 log reduction) was achieved only in combination with Chl, even at concentrations as low as 10 µM. Addition of 100 µg/mL Sm to media did not significantly increase the efficacy of the photodynamic treatment. This study proves principle that PDI can be used to treat plant diseases even if causative bacteria are resistant to conventional treatment. Therefore, PDI based on natural photosensitizers might represent an eco-friendly treatment strategy especially in organic farming.
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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 | 0.000 |
| 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 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".