The use of bovine serum albumin to improve the RT-qPCR detection of foodborne viruses rinsed from vegetable surfaces
Bibliographic record
Abstract
AIMS: To demonstrate that produce rinsates used for RT-qPCR detection of foodborne viruses may cause significant PCR inhibition and propose a means to reduce its impact on sensitivity. METHODS AND RESULTS: Here, it is shown that rinsing and concentration from spinach and precut lettuce have the potential to generate RNA extracts that are inhibitory to RT-qPCRs assembled from commercial kits for the detection of norovirus GII (NoV GII), hepatitis A virus (HAV), hepatitis E virus (HEV), rotavirus (RV) and feline calicivirus (FCV) as sample process control. It is further shown that the addition of bovine serum albumin (BSA) to those reactions restored a positive signal in all cases. The effect of BSA was dependent upon the primer/probe combination. Moreover, two of the detection systems (FCV and HAV) strongly benefited from the addition of BSA even in the absence of PCR inhibitors. CONCLUSIONS: BSA was shown to restore positive signals in five different RT-qPCR systems that were otherwise completely inhibited by produce rinsate extracts. It is therefore suggested to consider the addition of BSA to RT-qPCRs for the detection of foodborne viruses when inhibition is observed. SIGNIFICANCE AND IMPACT OF THE STUDY: This study clearly demonstrates the potency of PCR inhibitors generated during routine virus concentration from produce and that it can be alleviated by the addition of BSA to the RT-qPCRs. Although used elsewhere, the addition of BSA to PCRs is not a common practice in this growing field of research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".