Best Viral Elution Method Available for Quantification of Enteroviruses in Sludge by Both Cell Culture and Reverse Transcription-PCR
Bibliographic record
Abstract
The aim of this study was to select one or several virus extraction techniques that enable simultaneous detection of enterovirus genomes and infectious particles in different types of urban sludge. Eight techniques were compared by using 16 different liquid and solid sludge samples. The numbers of infectious enteroviruses in cell cultures were determined by using the most-probable-number method. The enterovirus genome was quantified by a single-tube reverse transcription-PCR using TaqMan technology. The results were statistically analyzed by Friedman's test, a nonparametric test for analysis of randomized block data using only ranks in terms of extraction technique efficiency. Two techniques seemed to yield higher viral titers as determined by simultaneous detection by cell culture and PCR. The first involved a 10% beef extract solution at pH 9 and sonication; the second involved a 0.3 M NaCl-7% beef extract solution at pH 7.5 followed by Freon treatment. In solid sludge, no significant differences were observed among the eight techniques tested. Both of the best techniques can be used for simultaneous detection of infectious enterovirus particles and genomes in any type of urban sludge.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".