Detection of SARS-CoV-2 in saliva: implications for specimen transport and storage
Bibliographic record
Abstract
Saliva has recently been proposed as a suitable specimen for the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Use of saliva as a diagnostic specimen may present opportunities for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) testing in remote and low-resource settings. Determining the stability of SARS-CoV-2 RNA in saliva over time is an important step in determining optimal storage and transport times. We undertook an in vitro study to assess whether SARS-CoV-2 could be detected in contrived saliva samples. The contrived saliva samples comprised 10 ml pooled saliva spiked with gamma-irradiated SARS-CoV-2 to achieve a concentration of 2.58×10 4 copies ml SARS-CoV-2, which was subsequently divided into 2 ml aliquots comprising: (i) neat saliva; and a 1 : 1 dilution with (ii) normal saline; (iii) viral transport media, and (iv) liquid Amies medium. Contrived samples were made in quadruplicate, with two samples of each stored at either: (i) room temperature or (ii) 4 °C. SARS-CoV-2 was detected in all SARS-CoV-2 spiked samples at time point 0, day 1, 3 and 7 at both storage temperatures using the N gene RT-PCR assay and time point 0, day 1 and day 7 using the Xpert Xpress SARS-CoV-2 (Cepheid, Sunnyvale, USA) RT-PCR assay. The ability to detect SARS-CoV-2 in saliva over a 1 week period is an important finding that presents further opportunities for saliva testing as a diagnostic specimen for the diagnosis of SARS-CoV-2.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".