Development, Optimization, and Validation of a Quantitative PCR Assay for Borrelia burgdorferi Detection in Tick, Wildlife, and Human Samples
Bibliographic record
Abstract
Tick-borne pathogens are growing in importance for human and veterinary research worldwide. We developed, optimized, and validated a reliable quantitative PCR (qPCR; real-time PCR) assay to assess Borrelia burgdorferi infection by targeting two B. burgdorferi genes, ospA and flaB. When assessing previously tested tick samples, its performance surpassed the nested PCR in efficiency, sensitivity, and specificity. Since the detection of Borrelia is more difficult in mammalian samples, the qPCR assay was also assessed using wildlife tissues. For wildlife samples, the sensitivity and specificity of ospA primers, with the incorporation of a pre-amplification step, was equivalent or superior to the nested PCR. For human samples, no primer set was successful with human tissue without culture, but we detected Borrelia with ospA and flaB primers in 50% of the Lyme culture samples, corresponding to 60% of the participants with a Lyme disease diagnosis or suspicion. The specificity of amplification was confirmed by Sanger sequencing. The healthy participant culture samples were negative. This PCR-based direct detection assay performs well for the detection of Borrelia in different biological samples. Advancements in detection methods lead to a better surveillance of Borrelia in vectors and hosts, and, ultimately, enhance human and animal health.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".