Direct PCR for Rapid and Safe Pathogen Detection: Laboratory Evaluation Supporting Field Use in Infectious Disease Outbreak
Bibliographic record
Abstract
Rapid, safe, and field-deployable molecular diagnostics are crucial for the effective management of infectious disease outbreaks, particularly those involving highly infectious pathogens, which can produce clinical symptoms similar to less infectious pathogens, thus raising potential biosafety concerns. In this study, we evaluated DNA/RNA Defend Pro (DRDP) buffer, a novel viral-inactivating transport medium designed to stabilize nucleic acids and allow direct PCR without nucleic acid extraction. To ensure critical qPCR parameters were not compromised by using DRDP, we conducted serial dilution tests using herpes simplex viruses 1 and 2 (HSV-1, HSV-2) and varicella-zoster virus (VZV), comparing DRDP to standard universal transport medium (UTM). Detection sensitivity, determined by cycle quantification (Cq) values, favored DRDP, as UTM samples required a 2–3-fold dilution to mitigate PCR inhibition. DRDP maintained reliable PCR compatibility at reaction volumes containing up to 25% buffer. At higher DRDP concentrations (30–35%), PCR inhibition occurred due to EDTA content but was fully reversible by adding supplemental magnesium. Furthermore, DRDP samples did not require an initial 95 °C thermal lysis step, thus simplifying the procedure without reducing PCR sensitivity or efficiency.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".