Comparison of quantitative PCR and digital PCR assays for quantitative detection of infectious bronchitis virus (IBV) genome
Bibliographic record
Abstract
The quantitative polymerase chain reaction (qPCR) technique is an extensively used molecular tool for the detection and quantification of viral genome load. However, since the qPCR assay is a relative quantification method that relies on an external calibration curve it has a lower assay precision and sensitivity. The digital PCR (dPCR) technique is a good alternative to the qPCR assay as it offers highly precise and direct quantification of viral genome load in samples. In this study, performance characteristics such as the quantification range, sensitivity, precision, and specificity of the dPCR technique was compared to qPCR technique for the detection and quantification of IBV genome loads in serial dilutions of IBV positive plasmid DNA, and IBV infected chicken tissue and swab samples. The quantification range of the qPCR assay was wider than that of the dPCR assay, however dPCR had a higher sensitivity compared to qPCR. The precision of quantification of DNA in plasmid samples in terms of repeatability and reproducibility of results was higher when using the dPCR assay compared to qPCR assay. The quantification results of IBV genome load in infected samples by the qPCR and dPCR assays displayed a high correlation. Hence, our findings suggest that dPCR could be used in avian virology research for improved precision and sensitivity in detection and quantification of viral genome loads.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".