Multicenter Comparison of Nucleic Acid Extraction Methods for Detection of Severe Acute Respiratory Syndrome Coronavirus RNA in Stool Specimens
Bibliographic record
Abstract
The emergence of a novel coronavirus (CoV) as the cause of severe acute respiratory syndrome (SARS) catalyzed the development of rapid diagnostic tests. Stool samples have been shown to be appropriate for diagnostic testing for SARS CoV, although it has been recognized to be a heterogeneous and difficult sample that contains amplification inhibitors. Limited information on the efficiency of extraction methods for the purification and concentration of SARS CoV RNA from stool samples is available. Our study objectives were to determine the optimal extraction method for SARS CoV RNA detection and to examine the effect of increased specimen volume for the detection of SARS CoV RNA in stool specimens. We conducted a multicenter evaluation of four automated and four manual extraction methods using dilutions of viral lysate in replicate mock stool samples, followed by quantitation of SARS CoV RNA using real-time reverse transcriptase PCR. The sensitivities of the manual methods ranged from 50% to 100%, with the Cortex Biochem Magazorb method, a magnetic bead isolation method, allowing detection of all 12 positive samples. The sensitivities of the automated methods ranged from 75% to 100%. The bioMérieux NucliSens automated extractor and miniMag extraction methods each had a sensitivity of 100%. Examination of the copy numbers detected and the generation of 10-fold dilutions of the extracted material indicated that a number of extraction methods retained inhibitory substances that prevented optimal amplification. Increasing the volume of sample input did improve detection. This information could be useful for the extraction of other RNA viruses from stool samples and demonstrates the need to evaluate extraction methods for different specimen types.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".