Rapid Methods for Detection and Enumeration of Campylobacter spp. in Foods
Bibliographic record
Abstract
Campylobacter spp. are the most commonly reported bacterial cause of acute diarrheal disease in humans throughout the world. Traditional cultural methods for the detection and quantitation of Campylobacterspp. are slow and tedious; therefore, specific, sensitive, and rapid methods for campylobacters are needed to collect sufficient data for risk assessment and food safety policy development. We developed several rapid methods based on polymerase chain reaction (PCR), DNA hybridization, hydrophobic grid membrane filters (HGMFs), and enzyme immunoassays (EIAs). A PCR assay targeting C. jejuni, combined with a simple sample preparation procedure, detects as few as 0.3 most probable number (MPN)/mL C. jejuni in naturally contaminated chicken rinses after 20-24 h enrichment. An HGMF-EIA method using a commercial polyclonal antibody for Campylobacter detects and enumerates thermophilic Campylobacter spp. from spiked chicken rinse and milk, and naturally contaminated chicken rinses. A C. jejuni-specific probe in an HGMF-DNA hybridization protocol specifically detects and quantitates C. jejuni in food samples. A dot-blot EIA combined with an MPN procedure quantitates thermophilic campylobacters from samples that might be difficult to filter through HGMFs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".