Phage receptor binding protein-based magnetic enrichment method as an aid for real time PCR detection of foodborne bacteria
Bibliographic record
Abstract
We present a novel phage receptor binding protein-based magnetic separation and pre-enrichment method as an alternative to the immunomagnetic separation methods by replacing antibodies with bacteriophage receptor binding proteins (RBPs). We couple the proposed RBP-based magnetic separation with real time PCR for rapid, sensitive and specific detection of Campylobacter jejuni cells in artificially contaminated skim milk, milk with 2% fat and chicken broth. Recovery rates, assessed by real time PCR, were greater than 80% for the samples spiked with as low as 100 cfu mL(-1) of C. jejuni cells. The specificity of capture was confirmed using Salmonella Typhimurium as a negative control where no bacteria were captured on the RBP-derivatized magnetic beads. The combination of RBP-based magnetic separation and real time PCR improved PCR sensitivity and allowed the detection of C. jejuni cells in milk and chicken broth samples without a time consuming pre-enrichment step through culturing. The total sample preparation and analysis time in the proposed RBP-based enrichment method coupled with real time PCR was less than 3 h.
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 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 | 0.000 |
| 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".