Conception and Optimization of Extraction-Free Loop-Mediated Isothermal Amplification Detection of Dry Rot Fungus <i>Serpula lacrymans</i>
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".