WarmStart colorimetric loop-mediated isothermal amplification for the one-tube, contamination-free and visualization detection of Shigella flexneri
Bibliographic record
Abstract
OBJECTIVES: Shigella flexneri (S. flexneri) is prevalent worldwide and the most common Shigella in many countries, causing highly contagious diarrhea, which seriously threatens public health. This study aimed to develop a colorimetric loop-mediated isothermal amplification (LAMP) for the rapid, accurate, and visualization detection of S. flexneri. METHODS: According to the screened specific genes of S. flexneri, three groups of LAMP primers were designed and evaluated, and the colorimetric LAMP reaction volume was optimized. The specificity of the colorimetric LAMP was validated by 20 S. flexneri and 96 non-S. flexneri clinical isolates. In addition, the sensitivity of the developed assay was evaluated by the serial 10-fold dilutions of plasmid DNA. RESULTS: A colorimetric LAMP assay was developed based on the specific S. flexneri hypothetical protein gene (Accession: AE014073 Region: 4170556.4171068). The colorimetric LAMP method had good specificity for detecting S. flexneri and enabled detection of S. flexneri within 30 minutes, with a plasmid detection limit of 7*10° copies/μL. The results of amplification could be easily identified by color. CONCLUSIONS: This colorimetric LAMP assay could be used for rapid and accurate diagnosis of S. flexneri infection, especially in remote hospitals and laboratories with under-equipped medical facilities, and in situations where an urgent diagnosis is needed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".