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Record W4403966886 · doi:10.1021/acsomega.4c05509

Conception and Optimization of Extraction-Free Loop-Mediated Isothermal Amplification Detection of Dry Rot Fungus <i>Serpula lacrymans</i>

2024· article· en· W4403966886 on OpenAlexaff
Vanessa L.S. LaPointe, Yvan Boutin, Frédéric Couture

Bibliographic record

VenueACS Omega · 2024
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsCentre intégré de santé et de services sociaux de Chaudière-AppalachesUniversité Laval
Fundersnot available
KeywordsDry rotLoop-mediated isothermal amplificationFungusExtraction (chemistry)BiologyBotanyChemistryChromatographyDNAGenetics

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The use of nucleic acid-based detection tools for microorganisms and fungi has become a gold standard. This is particularly the case for wood-decaying fungi like Serpula lacrymans, which are hard to discriminate based on macroscopic and microscopic observations. This dry rot is important to detect as it is particularly destructive in an infested building, which requires immediate action to prevent spreading and significant damage to structural elements. Through the development and optimization of loop-mediated isothermal amplification against S. lacrymans -specific rDNA internal transcribed spacer region, we demonstrate that it is possible to achieve rapid and specific amplification without nonspecific self-amplification in a similar range as real-time quantitative PCR without any necessary DNA isolation using a colorimetric detection assay. Through a combined set of self-amplification minimization along with hand-held sample homogenization, the LAMP assay was optimized to provide a femtogram-range assay capable of confirming identification in a real field sample either predominantly composed of S. lacrymans or containing the fungus while remaining negative when tested on different types of fungi found in basement-collected samples.

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 score0.688
Threshold uncertainty score0.415

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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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