Enzyme Treatment Reverse Transcription-PCR To Differentiate Infectious and Inactivated F-Specific RNA Phages
Bibliographic record
Abstract
F-specific (F+) RNA phages are recommended as indicators of fecal contamination and the presence of enteric viruses and as viral surrogates to elucidate the resistance of viruses to adverse conditions or to assess the effectiveness of inactivating processes. Reverse transcription (RT)-PCR methods have been used to detect, quantify, or identify subgroups of F+ RNA phages. However, these methods may overestimate the infectivity of F+ RNA phages in test samples, since the presence of both infectious and inactivated phages (or naked RNA) can lead to positive RT-PCR signals. In this study, we evaluated the ability of an enzyme treatment (ET) with proteinase K and RNase A prior to RNA extraction, followed by RT-PCR, to differentiate infectious and inactivated F+ RNA phages. The results indicated that ET RT-PCR reduced, but did not completely eliminate, false-positive signals encountered with RT-PCR alone. The two-step ET RT-PCR, in which the enzymes were added sequentially, was more effective at reducing false-positive signals than the one-step ET RT-PCR, which involved addition of both enzymes together. Despite its inability to completely eliminate false-positive signals, ET RT-PCR gave more reliable information on the infectivity of F+ RNA phages. Thus, the method is better than RT-PCR alone for detecting F+ RNA phages as indicators to assess the risk of fecal contamination by enteric pathogens or to evaluate the effectiveness of virus-inactivating processes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".