A fluorescence immunoassay for a rapid detection of Listeria monocytogenes on working surfaces
Bibliographic record
Abstract
Abstract Listeria monocytogenes is a foodborne pathogen responsible for human listeriosis. The increasing incidence of listeriosis induced governments and food manufacturing enterprises to act to diminish the problem. Several methods for the detection of Listeria monocytogenes in food industries were developed. However, they are time-consuming and require the use of specialized equipment. To reduce the detection time of Listeria monocytogenes in food, in this work we developed a fluorescence sandwich immunoassay based on the use of an innovative chitosan-cellulose nanocrystal (CNC) membrane that improves the antigen capture during bacterial growth. The combined use of CNC film for the capture of p60 protein-specific antigen together with the use of fluorescence detection reduced the time of analysis from 24 to 12 h with a limit of detection (LOD) of the assay of 102 CFU/mL (2 Log). In addition, the use of monoclonal anti-PepD covalently immobilized to a CNC membrane assured a high specificity of the assay. Interestingly, the obtained results show no cross-reactivity with the five most diffused pathogen bacteria strains tested.
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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 | 0.001 | 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 | 0.001 |
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".