Development of a Loop Mediated Isothermal Amplification (LAMP) - Surface Enhanced Raman spectroscopy (SERS) Assay for the Detection of <i>Salmonella Enterica</i> Serotype Enteritidis
Bibliographic record
Abstract
As a major foodborne pathogen, Salmonella enterica serotype Enteritidis is increasingly rising as a global health concern. Here, we developed an integrated assay that combines loop mediated isothermal amplification (LAMP) and surface enhanced Raman spectroscopy (SERS) for DNA detection of S. Enteritidis using specifically designed Raman active Au-nanoprobes. The target DNA was amplified by LAMP and then labeled with Au-nanoprobes comprised of gold nanoparticle-modified with specific cy5/DNA probes to allow the detection by SERS. The sensitivity of the developed LAMP-SERS detection assay (66 CFU/mL) was ~100-fold higher than the conventional polymerase chain reaction (PCR) method. Significantly, this technique allowed highly specific detection of the target DNA of S. Enteritidis and could differentiate it from the DNA of closely related bacterial species or non-specific contamination, making it more accurate and reliable than the standard LAMP technique. The applicability of detection of S. Enteritidis in milk samples using LAMP-SERS assay was validated as well. In sum, the developed LAMP-SERS assay is highly specific and sensitive, and has the potential to be applied for rapid detection of different foodborne pathogens and other microbial contaminants.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".