Comparison of salivary collection and processing methods for quantitative <scp>HHV</scp>‐8 detection
Bibliographic record
Abstract
OBJECTIVES: Saliva is a proved diagnostic fluid for the qualitative detection of infectious agents, but the accuracy of viral load determinations is unknown. Stabilising fluids impede nucleic acid degradation, compared with collection onto ice and then freezing, and we have shown that the DNA Genotek P-021 prototype kit (P-021) can produce high-quality DNA after 14 months of storage at room temperature. Here we evaluate the quantitative capability of 10 collection/processing methods. METHODS: Unstimulated whole mouth fluid was spiked with a mixture of HHV-8 cloned constructs, 10-fold serial dilutions were produced, and samples were extracted and then examined with quantitative PCR (qPCR). Calibration curves were compared by linear regression and qPCR dynamics. RESULTS: All methods extracted with commercial spin columns produced linear calibration curves with large dynamic range and gave accurate viral loads. Ethanol precipitation of the P-021 does not produce a linear standard curve, and virus is lost in the cell pellet. DNA extractions from the P-021 using commercial spin columns produced linear standard curves with wide dynamic range and excellent limit of detection. CONCLUSION: When extracted with spin columns, the P-021 enables accurate viral loads down to 23 copies μl(-1) DNA. The quantitative and long-term storage capability of this system makes it ideal for study of salivary DNA viruses in resource-poor settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".