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Record W4412687298 · doi:10.1002/ps.70012

Rapid detection of <scp><i>Hymenoscyphus fraxineus</i></scp> using genome‐specific primers based on loop‐mediated isothermal amplification assay

2025· article· en· W4412687298 on OpenAlexaff
Yu Zhou, Zan-Hua Zhu, Nicolas Feau, Yue Sun, Xu Liu, Wen Gao, Chongjuan You

Bibliographic record

VenuePest Management Science · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsLoop-mediated isothermal amplificationFraxinusBiologyGeneGenomePolymerase chain reactionGeneticsBotanyDNA

Abstract

fetched live from OpenAlex

BACKGROUND: Ash dieback, caused by Hymenoscyphus fraxineus (T. Kowalski) Baral, Queloz & Hosoya, is a serious fungal disease affecting Common ash (Fraxinus excelsior) populations across Europe and posing a global risk to ash trees worldwide. Traditional polymerase chain reaction (PCR)-based detection methods often target conserved gene regions, limiting the development of species-specific probes. However, the increasing availability of whole-genome sequences for H. fraxineus and its close relatives enables the identification of unique genes or genomic regions. RESULTS: This study utilized such genomic data to develop a sensitive, species-specific loop-mediated isothermal amplification (LAMP) assay for detecting H. fraxineus in Fraxinus mandshurica samples. The LAMP assay demonstrated a sensitivity of 2.53 fg/μL and proved effective in early detection of the pathogen. CONCLUSION: Altogether the developed LAMP detection method could be quite useful for further studies on the characteristics of the life cycle and colonization process of H. fraxineus in Asian ash host species. © 2025 Society of Chemical Industry.

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)0.002
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 score0.774
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.220
Teacher spread0.210 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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