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Record W4413128329 · doi:10.1111/ppa.70039

Harnessing Camalexin as a Sustainable and Ecofriendly Strategy to Control Harmful Phytopathogens

2025· article· en· W4413128329 on OpenAlexafffund
Farjana Rahman Lopa, Farzana Nazneen Snigdha, Rhea Amor Lumactud, Md Maniruzzaman Sikder

Bibliographic record

VenuePlant Pathology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsBiologyBiotechnology

Abstract

fetched live from OpenAlex

ABSTRACT Camalexin is a natural phytoalexin found in the Brassicaceae family, which has shown antimicrobial activity against diverse microbial pathogens. Plant pathogens are responsible for economic losses in agricultural productivity, and pesticides are being phased out across Europe, North America and other parts of the world due to environmental and human health concerns. Hence, sustainable strategies are a top priority for the management of pathogens. Different parts of the plant are known to produce natural antimicrobial compounds, such as camalexin, which can be used to control fungal, bacterial, nematode, viral and insect pests in an ecofriendly way. This review compiles data demonstrating the efficacy of camalexin against harmful pathogens and pests. This chemical acts as a fungistatic, fungicidal, bactericidal, insecticidal, nematocidal, antiviral and cytotoxic compound in a concentration‐dependent manner, primarily demonstrated in in vitro conditions. We also highlight investigations on the influence of camalexin on beneficial microbiota, suggesting a need for more research on non‐target microbiota. Future research is essential for elucidating the mechanisms underlying camalexin biosynthesis, toxicity and regulatory pathways, thereby revealing its potential as a sustainable crop protection strategy.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.235
Teacher spread0.229 · 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 teacher head, 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

Citations5
Published2025
Admission routes2
Has abstractyes

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