Evaluating and optimizing Acid-pH and Direct Lysis RNA extraction for SARS-CoV-2 RNA detection in whole saliva
Bibliographic record
Abstract
COVID-19 has been a global public health and economic challenge. Screening for the SARS-CoV-2 virus has been a key part of disease mitigation while the world continues to move forward, and lessons learned will benefit disease detection beyond COVID-19. Saliva specimen collection offers a less invasive, time- and cost-effective alternative to standard nasopharyngeal swabs. We optimized two different methods of saliva sample processing for RT-qPCR testing. Two methods were optimized to provide two cost-efficient ways to do testing for a minimum of four samples by pooling in a 2.0 mL tube and decrease the need for more highly trained personnel. Acid-pH-based RNA extraction method can be done without the need for expensive kits. Direct Lysis is a quick one-step reaction that can be applied quickly. Our optimized Acid-pH and Direct Lysis protocols are reliable and reproducible, detecting the beta-2 microglobulin (B2M) mRNA in saliva as an internal control from 97 to 96.7% of samples, respectively. The cycle threshold (Ct) values for B2M were significantly higher in the Direct Lysis protocol than in the Acid-pH protocol. The limit of detection for N1 gene was higher in Direct Lysis at ≤ 5 copies/μL than Acid-pH. Saliva samples collected over the course of several days from two COVID-positive individuals demonstrated Ct values for N1 that were consistently higher from Direct Lysis compared to Acid-pH. Collectively, this work supports that each of these techniques can be used to screen for SARS-CoV-2 in saliva for a cost-effective screening platform.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".