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Record W4205237630 · doi:10.1007/s43630-021-00157-0

Photodynamic Inactivation of plant pathogens part II: fungi

2022· article· en· W4205237630 on OpenAlexafffund
Christoph Hamminger, Michael Glueck, Michael Fefer, Wenzi Ckurshumova, Jun Liu, Raimund Tenhaken, Kristjan Plaetzer

Bibliographic record

VenuePhotochemical & Photobiological Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsSuncor Energy (Canada)
FundersUniversität SalzburgEnergy Policy and Planning OfficeSuncor Energy Incorporated
KeywordsBotrytis cinereaFungicideAlternaria solaniHorticulturePhotosensitizerSporeBiologyMicrobiologyChemistryFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The constantly increasing demand for agricultural produce from organic and conventional farming calls for new, sustainable, and biocompatible solutions for crop protection. The overuse of fungicides leading to contamination of both produce and environment and the emergence of plant pathogenic fungi that are resistant to conventional treatments warrant the need for new methods to combat fungal infections in the field. We here deliver the follow-up study to our research on the Photodynamic Inactivation (PDI) of plant pathogenic bacteria (Glueck et al. in Photochem Photobiol Sci 18(7):1700–1708, 2019) by expanding the scope to fungal pathogens. Both fungal species employed in this study— Alternaria solani and Botrytis cinerea —cause substantial crop and economic losses. Sodium magnesium chlorophyllin (Chl, approved as food additive E140) in combination with Na 2 EDTA and the chlorin e6 derivative B17-0024 holding cationic moieties serve as eco-friendly photoactive compounds. Effectiveness of the antifungal PDI was measured by inhibition of growth of mycelial spheres (average diameter 2–3 mm) after incubation with the photosensitizer for 100 min and subsequent illumination using a LED array (395 nm, 106.6 J cm −2 ). One hundred micromolar Chl combined with 5 mM Na 2 EDTA was able to successfully photokill 94.1% of A. solani and 91.7% of B. cinerea samples. PDI based on B17-0024 can completely inactivate A. solani at 10 times lower concentration (10 µM); however, for B. cinerea , the concentration required for complete eradication was similar to that of Chl with Na 2 EDTA (100 µM). Using a plant compatibility assay based on Fragaria vesca, we further demonstrate that both photosensitizers neither affect host plant development nor cause significant leaf damage. The plants were sprayed with 300 µL of treatment solution used for PDI (one or three treatments on consecutive days) and plant growth was monitored for 21 days. Only minor leaf damage was observed in samples exposed to the chelators Na 2 EDTA and polyaspartic acid, but overall plant development was unaffected. In conclusion, our results suggest that sodium magnesium chlorophyllin in combination with EDTA and B17-0024 could serve as effective and safe photofungicides. Graphical abstract

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.040
GPT teacher head0.316
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2022
Admission routes2
Has abstractyes

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